{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8fee91a2-2121-44ef-81b3-11ffc68db20d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in the Census tract data\n"
     ]
    }
   ],
   "source": [
    "#THIS FILE: revision of OH_muniSnap99 to use school districts, not munis as building blocks\n",
    "#vs OH_legisl_org, we build each HD from starter muni by contiguous munis based on fractional coverage\n",
    "\n",
    "from shapely.geometry import Point, LineString, Polygon, box\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import ast\n",
    "import time\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "tractPopFile = gpd.read_file(\"state_map_files/oh_pl2020_vtd.dbf\")  # ***USING VTDs ***\n",
    "tractPopFile.head()  #about 40 sec to load\n",
    "print(\"I read in the Census tract data\")\n",
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly,LW=1):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,lw=LW)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,lw=LW)  \n",
    "def plotCenter(t,geom,FONTSIZE=10):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t,ha='center',fontsize=FONTSIZE)\n",
    "    \n",
    "def r3(number):\n",
    "    result = round(number,3)\n",
    "    return result\n",
    "\n",
    "def r5(number):\n",
    "    result = round(number,5)\n",
    "    return result\n",
    "\n",
    "def getAdjoiners(UNITLIST, UNITNBRS): #returns all units that neighbor a UNITLIST but are not in the UNITLIST \n",
    "    allNbrs = list()\n",
    "    for U in UNITLIST:\n",
    "        allNbrs = allNbrs + UNITNBRS[U]\n",
    "    adjoiners = list( set(allNbrs).difference(set(UNITLIST)) )\n",
    "    return adjoiners\n",
    "\n",
    "def get2nbrs(ULIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    Get the combined list of first- and second-level neighbors of a LIST, using neighborList connectivity.  Excludes the source list\n",
    "    \"\"\"\n",
    "    firstList =  getAdjoiners(ULIST, UNITNBRS)\n",
    "    secondList = getAdjoiners(firstList, UNITNBRS)\n",
    "    twoLevelList = list( set(firstList + secondList).difference(set(ULIST) ) )\n",
    "    return twoLevelList\n",
    "\n",
    "def getContigFromStarter(starter, VLIST, NEIGHBORLIST):  #all contiguous units in VLIST, starting from starter unit\n",
    "    foundList, newList, prevList = [ starter ], [ starter ], [ starter ]\n",
    "    while len(newList) > 0:\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()        \n",
    "    return foundList   \n",
    "\n",
    "def isContiguous(VLIST,NEIGHBORLIST,returnBiggestPiece=False):\n",
    "    \"\"\"\n",
    "    This method determines if all items in a list form a continuous chain of neighbors based on the passed neighborlist\n",
    "    Empty lists are considered contiguous.  If discontiguous, a less-than-majority piece list is returned,\n",
    "     unless giveBig=True, in which case we return the biggest piece\n",
    "    \"\"\"    \n",
    "    isUnbroken, newList, foundList = True, list(), list()\n",
    "    if len(VLIST) > 0:\n",
    "        foundList, newList, prevList = [ VLIST[0] ], [ VLIST[0] ], [ VLIST[0] ]\n",
    "    while len(newList) > 0:  #this round's list of neighbors\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()      \n",
    "    pickedPieceList = foundList.copy()  #default contiguous sublist to pass back to main\n",
    "    \n",
    "    if len(foundList) < len(VLIST):  #not contiguous\n",
    "        isUnbroken  = False\n",
    "        pieceLists, remainingList = [foundList], list( set(VLIST).difference(set(foundList) ) )\n",
    "        if returnBiggestPiece == True:  #we were asked for the biggest piece, not a random small one, so we must find them all\n",
    "            if len(foundList) < 0.5*len(VLIST):\n",
    "                while len(remainingList) > 0:\n",
    "                    newList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                    pieceLists.append(newList)\n",
    "                    remainingList = list(set(remainingList).difference(set(newList)))\n",
    "                pieceLengths = [len(pL) for pL in pieceLists]\n",
    "                pickedPieceList = pieceLists[ pieceLengths.index(np.max(pieceLengths)) ]\n",
    "            \n",
    "        else: #quickly return a contiguous sub-list that's at most half the total units in the list\n",
    "            if len(foundList) > 0.5*len(VLIST): #pick a different piece; this one is the majority\n",
    "                pickedPieceList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                \n",
    "    return isUnbroken, pickedPieceList\n",
    "\n",
    "def enclaveCheck(UNITLIST,UNITNBRS,maxLoops=2):  #TRY 2 FOR OHIO V2.  USUALLY 4\n",
    "    \"\"\"\n",
    "    This method determines if a list of units has an unbroken boundary AND whether its complement has an unbroken boundary\n",
    "    If the complement boundary is broken, there is an enclave or the district is so disconnected its adjoining units aren't contiguous\n",
    "    The method returns contiguity of boundary and of its adjoiners.  Also returns the lists of boundary and complement-boundary units\n",
    "      If either of these is broken, only a contiguous sublist is returned that is guaranteed to be smaller than half (for finding enclaves)\n",
    "      maxLoops is the number of loops for expanding neighbors-of-neighbors for contiguity of the units outside the passed unit-list\n",
    "    \"\"\"\n",
    "    UNITSET = set(UNITLIST)\n",
    "    ADJLIST =  get2nbrs(UNITLIST, UNITNBRS)  #in case direct adjoiners are only queen-adjacent\n",
    "    #adjoinersOfAdjoiners = getAdjoiners(ADJLIST,  UNITNBRS)\n",
    "    #BDRYLIST = list( set(UNITLIST).intersection(set(adjoinersOfAdjoiners)) )  #this might fail for queen-adjacent boundary units\n",
    "    BDRYLIST = list (set(get2nbrs(ADJLIST,UNITNBRS)).intersection(UNITSET) )  #in case inHD boundary is only queen-adjacent\n",
    "    noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)  \n",
    "    unbroken, smallPieceList = isContiguous(BDRYLIST, UNITNBRS)\n",
    "    if not unbroken: #could be that district's boundary intersects the state boundary or there is a nonHD enclave inside the HD\n",
    "        unbroken, smallPieceList = isContiguous(UNITLIST, UNITNBRS)  #slower check for full district, not just its boundary \n",
    "    nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "    while nLoops <= maxLoops and not noEnclave:\n",
    "        nLoops +=1\n",
    "        newSet = set(ADJLIST)\n",
    "        for UU in ADJLIST:\n",
    "            newSet = newSet.union( set(UNITNBRS[UU]).difference(UNITSET) )\n",
    "        ADJLIST = list(newSet)\n",
    "        noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)\n",
    "    #isJiggy = noEnclave and unbroken\n",
    "    return unbroken, noEnclave, smallPieceList, enclaveList\n",
    "\n",
    "def getBdryNonEdgers(UNITLIST, UNITNBRS): #all in-district boundary units that neighbor a non-district unit\n",
    "    ALLnonHDnbrs = set()\n",
    "    for UUU in UNITLIST:\n",
    "        ALLnonHDnbrs = ALLnonHDnbrs.union(set(UNITNBRS[UUU])).difference(set(UNITLIST))\n",
    "    BdryNonEdgerSet = set()\n",
    "    for UUU in UNITLIST:\n",
    "        if len(set(UNITNBRS[UUU]).intersection(ALLnonHDnbrs)) > 0:\n",
    "            BdryNonEdgerSet.add(UUU)\n",
    "    return list(BdryNonEdgerSet)\n",
    "\n",
    "def getEnclaveLists(UNITLIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    finds ALL units enclaved by a UNITLIST, parsed into lists of contiguous pieces\n",
    "    We assume the largest contiguous complement to the UNITLIST is not an enclave, but rather the majority of the HD complement\n",
    "    if the UNITLIST's complement (all couldBeEnclaved) is contiguous, the returned list will be blank\n",
    "    \"\"\"\n",
    "    eSets, nU = list(), len(UNITNBRS)\n",
    "    offmapList = list()\n",
    "    for i,L in enumerate(UNITNBRS):\n",
    "        if len(L) == 0:\n",
    "            offmapList.append(i)  #offmap list are units with no neighbors (were surrounded)\n",
    "    complementSet = set([i for i in range(nU)] ).difference( set(UNITLIST + offmapList) )    \n",
    "    remaining2nbrs = get2nbrs(UNITLIST, UNITNBRS)\n",
    "    \n",
    "    isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS) #quicker than contig check on entire complement\n",
    "    while not isContig:  #this will kick out before writing the final sublist = map majority\n",
    "        starter = shortList[0]\n",
    "        newEnclaveList = getContigFromStarter(starter, list(complementSet), UNITNBRS)\n",
    "        eSets.append(set(newEnclaveList))\n",
    "        remaining2nbrs = list(set(remaining2nbrs).difference(set(shortList)) )\n",
    "        isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS)\n",
    "        \n",
    "        # couldBeEnclaved = list( set(couldBeEnclaved).difference(set(newEnclaveList)) )  #revised 18-Feb-24 -- was slower\n",
    "    fused_eSets = set()\n",
    "    for i, eSet in enumerate(eSets):\n",
    "        fused_eSets = fused_eSets.union(eSet)\n",
    "        for j in range(i+1, len(eSets) ) :\n",
    "            eSets[j] = eSets[j].difference(eSet)  #in case contiguity occurs, but not in 2-neighbor list; avoid double-count\n",
    "    remnant_eSet = complementSet.difference(fused_eSets) \n",
    "    eLengths = [len(eSet) for eSet in eSets]\n",
    "    if len(eSets) > 0:\n",
    "        if len(remnant_eSet) < np.max(eLengths):  #exchange the small remnant for the biggest found \"enclave\" (usually the major complement) \n",
    "            eSets[eLengths.index(np.max(eLengths))] = remnant_eSet.copy()\n",
    "    eLists = [list(eSet) for eSet in eSets]\n",
    "    return eLists\n",
    "\n",
    "def wontEnclave(proposedU, dList, NBRLIST, mapBDRYLIST):  #TRY MAXLOOPS = 3 FOR OH REDO, NOT USUAL 6\n",
    "    \"\"\"\n",
    "    This method checks if adding a proposedU to a dList (list of units in a district) will create an \"enclave\" of units in the dList's complement\n",
    "    via two problems:  A) the dList will now have a discontiguous set of units on the map boundary.  The full map boundary list is mapBDRYLIST\n",
    "      B) The non-dList neighbors of dList units aren't contiguous\n",
    "    The method doesn't require that the dList wontEnclave without the proposedU included; it just checks the proposedU + dList combination\n",
    "    It also doesn't check that the dList is itself contiguous with or without proposedU\n",
    "    In that sense, it is more limited than enclaveCheck\n",
    "    \"\"\"\n",
    "    wontEnclave = True\n",
    "    newList, newSet = dList + [proposedU], set(dList + [proposedU])\n",
    "    unitsOnBoundary = list( set(mapBDRYLIST).intersection(newSet) )\n",
    "    wontEnclave, __ = isContiguous(unitsOnBoundary,NBRLIST)  #first check - does district touch MAP boundary in multiple places? (quick FAIL)\n",
    "    if wontEnclave: #slower check below for internal enclaves\n",
    "        adjoiners = get2nbrs(newList, NBRLIST)  #2-level to protect for queen adjacency in boundary        \n",
    "        wontEnclave, __ = isContiguous(adjoiners,NBRLIST)\n",
    "        if not wontEnclave:\n",
    "            nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "            maxLoops, ADJLIST = 3, adjoiners #unlike enclaveCheck, here we just arbitrarily set a number of loops for search expansion\n",
    "            while nLoops <= maxLoops and not wontEnclave:\n",
    "                nLoops +=1\n",
    "                adjSet = set(ADJLIST)  #align nomenclature w enclaveCheck\n",
    "                for UU in ADJLIST:\n",
    "                    adjSet = adjSet.union( set(NBRLIST[UU]).difference(set(newList)) )\n",
    "                ADJLIST = list(adjSet)\n",
    "                wontEnclave, __ =   isContiguous(ADJLIST, NBRLIST)\n",
    "        \n",
    "    return wontEnclave\n",
    "\n",
    "def isRookAdj(geo1, geo2):  #intersection with rook (not queen) adjacency\n",
    "    isRookAdj = False\n",
    "    if geo1.intersects(geo2):\n",
    "        if (geo1.intersection(geo2)).geom_type != Point(0,0).geom_type:\n",
    "            isRookAdj = True\n",
    "    return isRookAdj\n",
    "\n",
    "def getWeightedAvgAndSD(LIST, WEIGHTS):\n",
    "    nWeights = len(WEIGHTS)\n",
    "    normWeights = [WEIGHTS[i] / np.sum(WEIGHTS) for i in range(nWeights) ]\n",
    "    AVG = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        AVG += normWeights[i] * value\n",
    "    sumVar = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        sumVar += normWeights[i] * (value - AVG)**2\n",
    "    SD = sumVar ** 0.5     #don't need to normalize again since weights were normalized\n",
    "    return AVG, SD\n",
    "\n",
    "def getHDcp(TRACTCP,TRACTPOP, TRACTLIST, SPLITTRACTNO = -777,SPLITTRACTUSE = 1.):  #population centerpoint of a Home District or county (cluster)\n",
    "    cpx, cpy, sumPop = 0.,0., 0.\n",
    "    for tt in TRACTLIST:\n",
    "        USE = 1.\n",
    "        if tt ==    SPLITTRACTNO:\n",
    "            USE =   SPLITTRACTUSE\n",
    "        sumPop += USE*TRACTPOP[tt]\n",
    "        cpx +=    USE*TRACTPOP[tt] * TRACTCP[tt].x\n",
    "        cpy +=    USE*TRACTPOP[tt] * TRACTCP[tt].y\n",
    "    HDCP_ = Point(cpx/sumPop, cpy/sumPop)\n",
    "    return HDCP_\n",
    "\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a9e2fbed-247e-4a00-be18-5507b7e2b88f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 8941 popn tracts for OH\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "STATE = \"OH\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "tractCountyNo = tractPopFile['COUNTYFP20']\n",
    "nTracts = len(tractPop)\n",
    "nVTDs = nTracts #nomenclature\n",
    "tractCP = [tractGeom[v].centroid for v in range(nVTDs) ]\n",
    "tractCPx, tractCPy = [tractCP[v].x for v in range(nVTDs)], [tractCP[v].y for v in range(nVTDs)]\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "tractCountyNo = tractCountyNo.to_numpy()\n",
    "countyNo = [0]*nTracts\n",
    "for t in range (0,nTracts) :  #from odd-numbered counties in US Census list to integer list\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "    countyNo[t] = int((int(tractCountyNo[t]) - 1)/2)\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)\n",
    "nCounties = int( np.max(countyNo)+1)\n",
    "skipList = [8197, 4634, 3730, 4458, 840, 2974, 1008]\n",
    "for t in skipList : #coastal vtds for Ohio already known\n",
    "    isSkippedTract[t] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a619e262-3e70-4945-87b1-58245cc514c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 0\n",
      "working on tract 1000\n",
      "working on tract 2000\n",
      "working on tract 3000\n",
      "working on tract 4000\n",
      "working on tract 5000\n",
      "working on tract 6000\n",
      "working on tract 7000\n",
      "working on tract 8000\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CREATE COUNTY-LEVEL MAP OF THIS STATE - excluding lakeshore tracts from county building\n",
    "for s in skipList :\n",
    "    countyNo[s] = -999  #don't count these lakeshore tracts as part of a county\n",
    "dummyPoly = Polygon([ (0,0),(1,0),(1,1)])\n",
    "countyGeom = [dummyPoly]*nCounties\n",
    "lakeErieGeom = []\n",
    "countyPop = [0.]*nCounties\n",
    "nCountyTracts = [0]*nCounties  #how many tracts found in this county\n",
    "countyTractList = [list() for c in range(nCounties) ]\n",
    "for t in range(nTracts):  #now loop through tracts, assign each to county, build county polygons\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if t not in skipList:\n",
    "        c = int(countyNo[t])\n",
    "        countyTractList[c].append(t)\n",
    "        nCountyTracts[c] +=1   #found another tract that belongs to this county\n",
    "        countyPop[c] += tractPop[t]\n",
    "        if nCountyTracts[c] == 1:  #this is the first tract found for this county\n",
    "            countyGeom[c] = tractGeom[t]  #seed the county geometry polygon\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "countyMAP = countyGeom[0]\n",
    "for c in range(nCounties):  #now build the state map from the counties\n",
    "    plotPoly(countyGeom[c])\n",
    "    plotCenter(c,countyGeom[c],8)\n",
    "    countyMAP = countyMAP.union(countyGeom[c])\n",
    "plotPoly(countyMAP)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0bacf56a-d610-40d7-83a8-fe6ff799a41c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now determine the county topology\n"
     ]
    }
   ],
   "source": [
    "print(\"Now determine the county topology\")\n",
    "countyNeighbors = [list() for c in range(nCounties)]\n",
    "for c in range(nCounties):\n",
    "    for cc in range(c+1, nCounties):\n",
    "        if isRookAdj(countyGeom[c],countyGeom[cc]):\n",
    "            countyNeighbors[c].append(cc)\n",
    "            countyNeighbors[cc].append(c)\n",
    "mainMAP = countyMAP.geoms[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cda12941-0eef-4a29-a376-dd0d4bc2927b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99 districts, each with avgDistrictPop of 119186.343\n",
      "I will now build a map from the counties\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#quick block to flag the whole county-districts -- though we do not use any whole-county units in this version\n",
    "MAP = dummyPoly\n",
    "#perfectCounty = [0]*nCounties  #\"perfect\" counties have the perfect population for a whole district\n",
    "#wholeCounty = [0]*nCounties    #\"whole\" counties are either perfect or small enough that they should be kept whole\n",
    "#minCountyRatio = 0.15   #not designated in this code\n",
    "#lockedTract = [0]*nTracts\n",
    "nDistricts = 99 #Ohio statehouse\n",
    "statePop = np.sum(tractPop)\n",
    "avgDistrictPop = statePop/nDistricts\n",
    "aDP = avgDistrictPop\n",
    "maxDevn = 0.05\n",
    "print(nDistricts,\"districts, each with avgDistrictPop of\",r3(avgDistrictPop))\n",
    "print(\"I will now build a map from the counties\") # and display the whole county districts\")\n",
    "for c in range(nCounties):\n",
    "    if MAP == dummyPoly:\n",
    "        MAP = countyGeom[c]\n",
    "    else:\n",
    "        MAP = MAP.union(countyGeom[c])\n",
    "    #if countyPop[c] >= (1.-maxDevn)*avgDistrictPop and countyPop[c] <= (1.+maxDevn)*avgDistrictPop :\n",
    "    #    perfectCounty[c] = 1\n",
    "    #    wholeCounty[c] = 1\n",
    "    #    plotPoly(countyGeom[c])\n",
    "    #if countyPop[c] < minCountyRatio*avgDistrictPop:  #small counties will naturally be unsplit.\n",
    "    #    wholeCounty[c] = 1\n",
    "plotPoly(MAP)\n",
    "plt.show()\n",
    "#print(\"there are a total of\",np.sum(wholeCounty),\"small or perfect counties that we will keep whole in Home Districts\")\n",
    "#for t in range(nTracts):\n",
    "#    if isSkippedTract == 0:\n",
    "#        if wholeCounty[countyNo[t]] == 1:  #this tract is part of a whole county per Article XI.3.c.2\n",
    "#            lockedTract[t] = 1             # ... or in a county that is so small we are keeping that county whole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "29bf3b47-56e7-4d50-939a-af1c51b28b81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we need to read in the school district shapes and assign VTDs to SDs\n",
      "assigning VTDs to school district number 0\n",
      "assigning VTDs to school district number 100\n",
      "assigning VTDs to school district number 200\n",
      "assigning VTDs to school district number 300\n",
      "assigning VTDs to school district number 400\n",
      "assigning VTDs to school district number 500\n",
      "assigning VTDs to school district number 600\n"
     ]
    }
   ],
   "source": [
    "print(\"Now we need to read in the school district shapes and assign VTDs to SDs\")\n",
    "sdDF = gpd.read_file(\"./state_map_files/tl_2021_39_unsd.dbf\")\n",
    "sdGeom = sdDF[\"geometry\"]\n",
    "nSDs = len(sdGeom)\n",
    "nCOIs, COIgeom = nSDs, sdGeom.copy() #nomenclature\n",
    "sdVTDlist = [list() for s in range(nSDs)]\n",
    "tractMuniNo = [-999]*nTracts\n",
    "for s in range(nSDs):\n",
    "    if s%100 == 0:\n",
    "        print(\"assigning VTDs to school district number\",s)\n",
    "    for c in range(nCounties):\n",
    "        if sdGeom[s].intersects(countyGeom[c]):\n",
    "            for v in countyTractList[c]:\n",
    "                if sdGeom[s].contains(tractCP[v]):\n",
    "                    sdVTDlist[s].append(v)\n",
    "                    tractMuniNo[v] = s\n",
    "muniTractList = [sdVTDlist[s].copy() for s in range(nSDs)] #nomenclature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "97b6f366-d522-4b4b-b788-c0364dbdfaf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 612 COIs / school districts\n",
      "There were a total of 1 low-pop non-coastal bgs not captured by a schoolD\n",
      "I will now assign them to the contiguous SD with the closest centroid\n",
      "[1415]\n"
     ]
    }
   ],
   "source": [
    "print(\"there are a total of\",nCOIs,\"COIs / school districts\")\n",
    "missedVTDlist = list()\n",
    "for v in range(nTracts):\n",
    "    if v not in skipList and tractMuniNo[v] < 0:\n",
    "        missedVTDlist.append(v)\n",
    "print(\"There were a total of\",len(missedVTDlist),\"low-pop non-coastal bgs not captured by a schoolD\")\n",
    "print(\"I will now assign them to the contiguous SD with the closest centroid\")\n",
    "for b in missedVTDlist:\n",
    "    closeCOIs = list()\n",
    "    for c in range(nCOIs):\n",
    "        if isRookAdj(tractGeom[b],COIgeom[c]):\n",
    "            closeCOIs.append(c)\n",
    "    closeDists = [tractGeom[b].distance(COIgeom[c].centroid) for c in closeCOIs]\n",
    "    cc = closeCOIs[closeDists.index(np.min(closeDists))]\n",
    "    tractMuniNo[b] = cc\n",
    "    muniTractList[cc].append(b)\n",
    "    COIgeom[cc] = COIgeom[cc].union(tractGeom[b])\n",
    "\n",
    "for b in range(nTracts):\n",
    "    if tractMuniNo[b] < 0 and b not in skipList:\n",
    "        plotPoly(tractGeom[b])\n",
    "        print(\"unassigned noncoastal blockgroup\",b,countyNo[b],tractPop[b])\n",
    "print(missedVTDlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "d244db11-e615-4b99-bd0c-c716575410ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a histogram of number of vtds per COI\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the minimum number of vtds in a COI is 0\n"
     ]
    }
   ],
   "source": [
    "print(\"here is a histogram of number of vtds per COI\")\n",
    "plt.hist([len(muniTractList[u]) for u in range(nCOIs)],bins = [0,1,3,10,30,300,1000] )\n",
    "plt.show()\n",
    "plt.scatter([u for u in range(nCOIs)], [len(muniTractList[u]) for u in range(nCOIs)] )\n",
    "plt.show()\n",
    "print(\"the minimum number of vtds in a COI is\",np.min([len(muniTractList[u]) for u in range(nCOIs)]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f9a51f78-9081-47e9-9ca0-21314c7e451f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on vtd neighbors in county 0\n",
      "working on vtd neighbors in county 10\n",
      "working on vtd neighbors in county 20\n",
      "working on vtd neighbors in county 30\n",
      "working on vtd neighbors in county 40\n",
      "working on vtd neighbors in county 50\n",
      "working on vtd neighbors in county 60\n",
      "working on vtd neighbors in county 70\n",
      "working on vtd neighbors in county 80\n",
      "finding Kelleys Island\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vtdNbrs = [list() for v in range(nTracts)]\n",
    "for c in range(nCounties):\n",
    "    if c%10 == 0:\n",
    "        print(\"working on vtd neighbors in county\",c)\n",
    "    for v in countyTractList[c]:\n",
    "        for vv in set(countyTractList[c]).difference({v}):\n",
    "            if isRookAdj(tractGeom[vv], tractGeom[v]):\n",
    "                vtdNbrs[v].append(vv)\n",
    "        for cc in countyNeighbors[c]:\n",
    "            if isRookAdj(countyGeom[cc],tractGeom[v]):\n",
    "                for vv in countyTractList[cc]:\n",
    "                    if isRookAdj(tractGeom[vv], tractGeom[v]):\n",
    "                        vtdNbrs[v].append(vv)\n",
    "for v in countyTractList[21]:\n",
    "    plotPoly(tractGeom[v])\n",
    "    plotCenter(v,tractGeom[v])\n",
    "print(\"finding Kelleys Island\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a94e8cb9-605f-481b-a003-7ab6d02a319a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for v in [3739,3622,3627]:\n",
    "    plotPoly(tractGeom[v])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "bd772b18-8303-457c-8551-5ecd60515903",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "manually connecting Kelleys Island bg to mainland\n"
     ]
    }
   ],
   "source": [
    "print(\"manually connecting Kelleys Island bg to mainland\")\n",
    "Kelley, Knbrs = 3739, [3622, 3627] #FILL THIS IN BASED ON ABOVE 5879, [5870, 5871]\n",
    "for v in Knbrs:\n",
    "    vtdNbrs[Kelley].append(v)\n",
    "    vtdNbrs[v].append(Kelley)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "0e0cefa1-dad2-471e-ac40-51655a0eb89a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we also need to connect Catawba to Put-in-Bay\n"
     ]
    }
   ],
   "source": [
    "print(\"we also need to connect Catawba to Put-in-Bay\")\n",
    "vtdNbrs[1006].append(926)\n",
    "vtdNbrs[926].append(1006)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "eccac5a2-f8c1-470a-8740-e3d191a4b8f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "bgNbrs = [vtdNbrs[v].copy() for v in range(nVTDs)] #nomenclature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "6feefb20-60a0-4e80-9907-75dcfe87a526",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check - all vtd assigned?\n",
      "8941 8941\n"
     ]
    }
   ],
   "source": [
    "print(\"check - all vtd assigned?\")\n",
    "print(len(tractPop), len(skipList) + np.sum([len(muniTractList[uu]) for uu in range(nCOIs) ]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "5a92ffd2-6e0d-4878-8a32-5d7a904830b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will splinter any noncontiguous COIs / SDs into their contig parts.  County-straddlers are OK.\n",
      "first, ID the non-rook contig units ...\n",
      "we found a total of 24 Units that were not rook-contiguous\n"
     ]
    }
   ],
   "source": [
    "print(\"we will splinter any noncontiguous COIs / SDs into their contig parts.  County-straddlers are OK.\")\n",
    "print(\"first, ID the non-rook contig units ...\")\n",
    "isContigUnit = [False]*nCOIs\n",
    "nonContigUnits = list()\n",
    "for c in range(nCOIs):\n",
    "    isContigUnit[c] = isContiguous(muniTractList[c],vtdNbrs)[0]\n",
    "    if not isContigUnit[c]:\n",
    "        nonContigUnits.append(c)\n",
    "print(\"we found a total of\",len(nonContigUnits),\"Units that were not rook-contiguous\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "c63283ed-3fcb-4e5b-aaf0-237fd1a8b891",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "split out these noncontig COIs, like fragmented cities\n",
      "after splitting the 24 queen-adj units, we have 657 units\n",
      "check - all vtds assigned?\n",
      "8941 8941\n"
     ]
    }
   ],
   "source": [
    "print(\"split out these noncontig COIs, like fragmented cities\")\n",
    "for u in nonContigUnits:\n",
    "    nVTDsAssigned, nVTDsAvailable = 0, len(muniTractList[u])\n",
    "    contigList = getContigFromStarter(muniTractList[u][0],muniTractList[u],vtdNbrs)\n",
    "    remainList = list(set(muniTractList[u]).difference(set(contigList)) )\n",
    "    muniTractList[u] = contigList.copy()\n",
    "    nVTDsAssigned += len(contigList)\n",
    "    while len(remainList) > 0:\n",
    "        contigList = getContigFromStarter(remainList[0],remainList,vtdNbrs)\n",
    "        muniTractList.append(contigList.copy())\n",
    "        nVTDsAssigned += len(contigList)\n",
    "        remainList = list(set(remainList).difference(set(contigList)) )\n",
    "    if nVTDsAssigned != nVTDsAvailable:\n",
    "        print(\"oops, for unit\",u,\"only\",nVTDsAssigned,\"were assigned out of\",nVTDsAvailable)\n",
    "nCOIs = len(muniTractList)\n",
    "print(\"after splitting the\",len(nonContigUnits),\"queen-adj units, we have\",nCOIs,\"units\")  \n",
    "print(\"check - all vtds assigned?\")\n",
    "print(len(tractPop), len(skipList) + np.sum([len(muniTractList[uu]) for uu in range(nCOIs) ]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "8ec542bf-6a14-4c15-a722-96917f993290",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we have a total of 3 units with no vtds and 30 units with 1 vtd\n",
      "Here are the COIs with zero vtds [408, 433, 434]\n",
      "after eliminating these zeros, we have 654 COIs\n"
     ]
    }
   ],
   "source": [
    "nZero, nOne = 0,0\n",
    "zeroList, oneList = list(), list()\n",
    "for u in range(nCOIs):\n",
    "    if len(muniTractList[u]) == 0:\n",
    "        nZero +=1\n",
    "        zeroList.append(u)\n",
    "    if len(muniTractList[u]) == 1:\n",
    "        nOne +=1\n",
    "        oneList.append(u)\n",
    "print(\"we have a total of\",nZero,\"units with no vtds and\",nOne,\"units with 1 vtd\")\n",
    "print(\"Here are the COIs with zero vtds\",zeroList)\n",
    "\n",
    "oldTractList = [muniTractList[c].copy() for c in range(nCOIs)]\n",
    "muniTractList = list()\n",
    "for c in range(nCOIs):\n",
    "    if len(oldTractList[c]) > 0:\n",
    "        muniTractList.append(oldTractList[c])\n",
    "nCOIs = len(muniTractList)\n",
    "print(\"after eliminating these zeros, we have\",nCOIs,\"COIs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "dbb7b900-8997-44c1-ba12-559a7e071b88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "compute and plot the COI pops by area, ID the big COIs\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found a COI with pop/aDP = 1.528\n",
      "found a COI with pop/aDP = 1.236\n",
      "found a COI with pop/aDP = 1.21\n",
      "found a COI with pop/aDP = 1.75\n",
      "found a COI with pop/aDP = 4.463\n",
      "found a COI with pop/aDP = 2.854\n",
      "found a COI with pop/aDP = 3.096\n",
      "there are 7 COIs with pop > 1.05 * 119186.34343434343\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after splitting the 7 big COIs, we have a total of 2015 units\n",
      "building vtd-based COIgeom for unit 0\n",
      "building vtd-based COIgeom for unit 100\n",
      "building vtd-based COIgeom for unit 200\n",
      "building vtd-based COIgeom for unit 300\n",
      "building vtd-based COIgeom for unit 400\n",
      "building vtd-based COIgeom for unit 500\n",
      "building vtd-based COIgeom for unit 600\n",
      "building vtd-based COIgeom for unit 700\n",
      "building vtd-based COIgeom for unit 800\n",
      "building vtd-based COIgeom for unit 900\n",
      "building vtd-based COIgeom for unit 1000\n",
      "building vtd-based COIgeom for unit 1100\n",
      "building vtd-based COIgeom for unit 1200\n",
      "building vtd-based COIgeom for unit 1300\n",
      "building vtd-based COIgeom for unit 1400\n",
      "building vtd-based COIgeom for unit 1500\n",
      "building vtd-based COIgeom for unit 1600\n",
      "building vtd-based COIgeom for unit 1700\n",
      "building vtd-based COIgeom for unit 1800\n",
      "building vtd-based COIgeom for unit 1900\n",
      "building vtd-based COIgeom for unit 2000\n"
     ]
    }
   ],
   "source": [
    "COItractList = [muniTractList[c].copy() for c in range(nCOIs)]  #nomenclature\n",
    "print(\"compute and plot the COI pops by area, ID the big COIs\")\n",
    "COIpop =  [np.sum([tractPop[t] for t in muniTractList[u] ]) for u in range(nCOIs)]\n",
    "COIarea = [np.sum([tractArea[t] for t in muniTractList[u] ]) for u in range(nCOIs)]\n",
    "plt.scatter(COIarea,COIpop)\n",
    "plt.axhline(aDP,ls=\"--\")\n",
    "plt.show()\n",
    "isBigCOI, bigCOIlist = [0]*nCOIs,list()\n",
    "for u in range(nCOIs):\n",
    "    if COIpop[u] > 1.05 * aDP:  #1.01 for Congrl, 1.05 for legisl\n",
    "        isBigCOI[u] = 1\n",
    "        bigCOIlist.append(u)\n",
    "        print(\"found a COI with pop/aDP =\",r3(COIpop[u]/aDP) )\n",
    "nBigCOIs = len(bigCOIlist)\n",
    "print(\"there are\",nBigCOIs,\"COIs with pop > 1.05 *\",aDP)\n",
    "\n",
    "COIgeom = [tractGeom[muniTractList[c][0]] for c in range(nCOIs) ]  # we will only build the big ones, but have to start them\n",
    "for c in bigCOIlist:    \n",
    "    for v in muniTractList[c]:\n",
    "        COIgeom[c] = COIgeom[c].union(tractGeom[v])\n",
    "    plotPoly(COIgeom[c])\n",
    "    plotCenter(r3(COIpop[c]/aDP), COIgeom[c])\n",
    "plotPoly(MAP)\n",
    "plt.show()\n",
    "\n",
    "for u in bigCOIlist:  #now spli them up\n",
    "    for i,v in enumerate(muniTractList[u].copy()) :\n",
    "        if i == 0:\n",
    "            muniTractList[u] = [v]\n",
    "        else:\n",
    "            muniTractList.append([v])\n",
    "nUnits = len(muniTractList)\n",
    "print(\"after splitting the\",nBigCOIs,\"big COIs, we have a total of\",nUnits,\"units\")\n",
    "\n",
    "unitGeom = [tractGeom[muniTractList[u][0]] for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    unitPop = np.sum([tractPop[v] for v in muniTractList[u] ] )\n",
    "    if u%100 == 0:\n",
    "        print(\"building vtd-based COIgeom for unit\",u)\n",
    "    for v in muniTractList[u]:\n",
    "        unitGeom[u] = unitGeom[u].union(tractGeom[v])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "7254555b-bf6a-4ccb-8d5d-628d56773723",
   "metadata": {},
   "outputs": [],
   "source": [
    "unitTractList = [muniTractList[u].copy() for u in range(nUnits)] #nomenclature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "aaa8dc85-9a68-4e49-992d-42c8c5b52254",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create the unit topology. First assign each vtd's unit number (reverse of unitTractList)\n",
      "build unit nbr list based on adjacent vtd's\n"
     ]
    }
   ],
   "source": [
    "print(\"Create the unit topology. First assign each vtd's unit number (reverse of unitTractList)\")\n",
    "tractUnitNo = [-999 for v in range(nTracts)]\n",
    "for u in range(nUnits):\n",
    "    for v in unitTractList[u]:\n",
    "        tractUnitNo[v] = u\n",
    "unitNbrSet = [set() for u in range(nUnits)]\n",
    "print(\"build unit nbr list based on adjacent vtd's\")  #these are list-based, not geom-based\n",
    "for v in range(nTracts):\n",
    "    u = tractUnitNo[v]\n",
    "    for vv in vtdNbrs[v]:\n",
    "        uu = tractUnitNo[vv]\n",
    "        if uu != u  and uu >= 0:  #avoid assigning vtd's relegated to unit -999\n",
    "            unitNbrSet[u].add(uu)\n",
    "unitNbrs = [list(unitNbrSet[u]) for u in range(nUnits)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "5cb669f9-5e91-4fd2-8195-e0791a90466a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for u in range(nUnits):\n",
    "    if len(unitNbrs[u]) == 0:\n",
    "        print(\"a neighborless unit, and its county\")\n",
    "        print(u)\n",
    "        for v in unitTractList[u]:\n",
    "            plotPoly(tractGeom[v])\n",
    "            plotPoly(countyGeom[countyNo[v]])\n",
    "            c = countyNo[v]\n",
    "        for uu in range(nUnits):\n",
    "            if countyNo[unitTractList[uu][0]] == c:\n",
    "                for v in unitTractList[uu]:\n",
    "                    plotCenter(uu,tractGeom[v])\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "52325c6a-760e-4923-a1b1-df0dadf69a04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[497]\n"
     ]
    }
   ],
   "source": [
    "print(unitNbrs[648])  #looks like I already added because I used updated vtdNbr list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1ebd5b0d-e94f-4c5a-8ff4-7c0595ebdcf0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "based on above, connect the island to the mainland ...\n"
     ]
    }
   ],
   "source": [
    "print(\"based on above, connect the island to the mainland ...\")\n",
    "unitNbrs[497].append(648)\n",
    "unitNbrs[648].append(497)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "437c1607-5cb6-49b0-855d-45bdda404f1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let's find singles and small clusters surrounded by big units\n",
      "This is a more generalized surround operation\n",
      "checking for big surround for unit 0\n",
      "checking for big surround for unit 500\n",
      "checking for big surround for unit 1000\n",
      "checking for big surround for unit 1500\n",
      "checking for big surround for unit 2000\n",
      "I found a total of 20 surrounded units. nSurrounders = 15\n"
     ]
    }
   ],
   "source": [
    "print(\"Now let's find singles and small clusters surrounded by big units\")\n",
    "print(\"This is a more generalized surround operation\")\n",
    "surrounded, surrounders = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if u%500 == 0:\n",
    "        print(\"checking for big surround for unit\",u)\n",
    "    nbrNbrs = [len(unitNbrs[uu]) for uu in unitNbrs[u]]\n",
    "    bigNbrU = unitNbrs[u][nbrNbrs.index(np.max(nbrNbrs))]  #the surrounder should have more neighbors that surrounded\n",
    "    loopNo, foundSet = 0, set(unitNbrs[u]).difference({bigNbrU})\n",
    "    while len(foundSet) < 20 and loopNo < 7:\n",
    "        loopNo +=1\n",
    "        for uu in foundSet:\n",
    "            foundSet = foundSet.union(set(unitNbrs[uu]).difference({bigNbrU}) )\n",
    "    if len(foundSet) < 20:\n",
    "        surrounded.append(u)\n",
    "        surrounders.append(bigNbrU)\n",
    "print(\"I found a total of\",len(surrounded),\"surrounded units. nSurrounders =\",len(set(surrounders)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "5650516e-01b0-4e86-b69b-4e77ff5c8317",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[407] are both surrounders and surrounded\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sandwichSurrounds = list()\n",
    "for u in surrounded:\n",
    "    if u in surrounders:\n",
    "        sandwichSurrounds.append(u)\n",
    "print(sandwichSurrounds,\"are both surrounders and surrounded\")\n",
    "for u in sandwichSurrounds:\n",
    "    plotPoly(unitGeom[u])\n",
    "    plotCenter(u,unitGeom[u])\n",
    "    for uu in unitNbrs[u]:\n",
    "        plotPoly(unitGeom[uu],0.2)\n",
    "        plotCenter(uu,unitGeom[uu],8)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "aad9cf17-f19a-4e88-b497-3197793074bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now capture surrounds.  If any are both surrounders and surrounded, we handle them in second loop\n",
      "407 is both a surrounder and surrounded.  Handle this one later\n",
      "now working on sandwichSurround 407\n",
      "now update unitPops and CPs\n",
      "statePop, updated sum unitPops now 11799448 11799448.0\n",
      "total assigned VTDs = 8934 + 7 vs 8941\n"
     ]
    }
   ],
   "source": [
    "print(\"now capture surrounds.  If any are both surrounders and surrounded, we handle them in second loop\")\n",
    "for i, u in enumerate(surrounded):\n",
    "    uu = surrounders[i]\n",
    "    if u in sandwichSurrounds:\n",
    "        print(u,\"is both a surrounder and surrounded.  Handle this one later\")\n",
    "    else:\n",
    "        unitNbrs[uu].remove(u)\n",
    "        unitNbrs[u] = list()\n",
    "        unitTractList[uu] += unitTractList[u]\n",
    "        for t in unitTractList[u]:\n",
    "            unitGeom[uu] = unitGeom[uu].union(tractGeom[t])\n",
    "            tractUnitNo[t] = uu\n",
    "        unitTractList[u] = list()\n",
    "for u in sandwichSurrounds:\n",
    "    print(\"now working on sandwichSurround\",u)\n",
    "    uu = surrounders[surrounded.index(u)]\n",
    "    unitNbrs[uu].remove(u)\n",
    "    unitNbrs[u] = list()\n",
    "    unitTractList[uu] += unitTractList[u]\n",
    "    for t in unitTractList[u]:\n",
    "        unitGeom[uu] = unitGeom[uu].union(tractGeom[t])\n",
    "        tractUnitNo[t] = uu\n",
    "    unitTractList[u] = list()\n",
    "print(\"now update unitPops and CPs\")\n",
    "unitPop = [np.sum([tractPop[b] for b in unitTractList[u] ]) for u in range(nUnits) ]\n",
    "unitCP = [unitGeom[u].centroid for u in range(nUnits) ]\n",
    "print(\"statePop, updated sum unitPops now\",np.sum(tractPop),np.sum(unitPop))\n",
    "print(\"total assigned VTDs =\",np.sum([len(unitTractList[u]) for u in range(nUnits)]),\"+\",len(skipList),\"vs\",nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "998a5951-61a4-414f-9443-4986a9176c10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, cut out the surrounded units and transfer their pops.  We already fused the geoms and bg lists\n",
      "To be safe, we will reassign unit nbrs from vtd topology \n",
      "state pop = 11799448 = 11799448\n",
      "reassign unit nbrs based on vtd topology\n",
      "build unit nbr list based on adjacent vtd's\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, cut out the surrounded units and transfer their pops.  We already fused the geoms and bg lists\")\n",
    "print(\"To be safe, we will reassign unit nbrs from vtd topology \")\n",
    "oldPop, oldGeom, oldList = unitPop.copy(), unitGeom.copy(), [unitTractList[u].copy() for u in range(nUnits)]\n",
    "oldNbrs = [unitNbrs[u].copy() for u in range(nUnits) ]\n",
    "\n",
    "unitPop, unitGeom, unitTractList, oldUnitNo, nOldUnits = list(), list(), list(), list(), nUnits\n",
    "for u in range(nOldUnits):\n",
    "    if u not in surrounded:\n",
    "        oldUnitNo.append(u)\n",
    "        unitPop.append(np.sum([tractPop[t] for t in oldList[u] ]) )\n",
    "        unitGeom.append(oldGeom[u])\n",
    "        unitTractList.append(oldList[u].copy())\n",
    "nUnits = len(unitPop)\n",
    "    \n",
    "tractUnitNo = [-999 for t in range(nTracts)]  #need to reset after surrounds\n",
    "for u in range(nUnits):\n",
    "    for v in unitTractList[u]:\n",
    "        tractUnitNo[v] = u\n",
    "print(\"state pop =\",np.sum(tractPop),\"=\",np.sum([np.sum([tractPop[b] for b in unitTractList[u] ]) for u in range(nUnits)]) )\n",
    "\n",
    "print(\"reassign unit nbrs based on vtd topology\")\n",
    "unitNbrSet = [set() for u in range(nUnits)]\n",
    "print(\"build unit nbr list based on adjacent vtd's\")  #these are list-based, not geom-based\n",
    "for v in range(nTracts):\n",
    "    u = tractUnitNo[v]\n",
    "    for vv in vtdNbrs[v]:\n",
    "        uu = tractUnitNo[vv]\n",
    "        if uu != u  and uu >= 0:  #avoid assigning vtd's relegated to unit -999\n",
    "            unitNbrSet[u].add(uu)\n",
    "unitNbrs = [list(unitNbrSet[u]) for u in range(nUnits)]\n",
    "unitCP = [unitGeom[u].centroid for u in range(nUnits)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "8e1d20ee-9b51-462d-88ec-7ce534024df7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "did we create any bigUs via surround?\n"
     ]
    }
   ],
   "source": [
    "print(\"did we create any bigUs via surround?\")\n",
    "for u in range(nUnits):\n",
    "    if unitPop[u] > 1.05 * aDP:\n",
    "        print(u,r3(unitPop[u]/aDP))\n",
    "        plotPoly(unitGeom[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "438694fa-7eb2-4628-8a32-08de211f79c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check for discontig units\n",
      "we found a total of 0 Units that were not rook-contiguous\n"
     ]
    }
   ],
   "source": [
    "print(\"check for discontig units\")\n",
    "isContigUnit = [False]*nUnits\n",
    "nonContigUnits = list()\n",
    "for c in range(nUnits):\n",
    "    isContigUnit[c] = isContiguous(unitTractList[c],vtdNbrs)[0]\n",
    "    if not isContigUnit[c]:\n",
    "        nonContigUnits.append(c)\n",
    "print(\"we found a total of\",len(nonContigUnits),\"Units that were not rook-contiguous\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "ff0c9aa3-1931-4e8c-8689-05018af239f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining border counties\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we determine and plot the border vtds\n",
      "IDing border vtds in county 0\n",
      "IDing border vtds in county 19\n",
      "IDing border vtds in county 49\n",
      "IDing border vtds in county 80\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"defining border counties\")\n",
    "\n",
    "Kelley, Knbrs = 3739, [3622, 3627] #FILL THIS IN BASED ON ABOVE 5879, [5870, 5871] for bgs\n",
    "#Kelley, Knbrs = 5879, [5870, 5871]  #in case we used previously saved bg topology vs. defining in this run\n",
    "borderCounties = list()\n",
    "for c in range(nCounties):\n",
    "    if countyGeom[c].intersects(mainMAP.exterior):\n",
    "        borderCounties.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "plt.show()\n",
    "usePrevious = False\n",
    "if usePrevious:\n",
    "    print(\"We will use previously defined and read-in border vtds\")\n",
    "else:\n",
    "    print(\"Now we determine and plot the border vtds\")\n",
    "    borderVTDs = list()\n",
    "    for i,c in enumerate(borderCounties):\n",
    "        if i%10 == 0:\n",
    "            print(\"IDing border vtds in county\",c)\n",
    "        for v in countyTractList[c]:\n",
    "            if isRookAdj(tractGeom[v],mainMAP.exterior) or v == Kelley:\n",
    "                borderVTDs.append(v)\n",
    "                plotPoly(tractGeom[v])\n",
    "    plt.show()\n",
    "    isBorderVTD = [0]*nTracts\n",
    "    for v in borderVTDs:\n",
    "        isBorderVTD[v] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "febb7f03-b536-4d17-8d61-ea4e9599af7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make a tentative set / list of border units. Numbered units have zero nonborder neighbors; will be surrounded next block\n",
      "'pop' shows pop/aDP for pop/aDP > 0.5 in a border unit\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiwAAAGdCAYAAAAxCSikAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAADbYklEQVR4nOzdd3wU1drA8d/MbEnvnZLQAyRAKAJKUxBQBEEUURSxF6zoq2JFxYvlXkXFigWxgVgQUZCiSBEEKdI7IUBIL5u2bWbePzZZEghkA6lwvn7WnXLmzNklyT57qqTruo4gCIIgCEIDJtd3AQRBEARBEKoiAhZBEARBEBo8EbAIgiAIgtDgiYBFEARBEIQGTwQsgiAIgiA0eCJgEQRBEAShwRMBiyAIgiAIDZ4IWARBEARBaPAM9V2AmqBpGqmpqfj7+yNJUn0XRxAEQRAED+i6TkFBATExMcjymetQzouAJTU1lWbNmtV3MQRBEARBOAtHjhyhadOmZ0xzXgQs/v7+gOsFBwQE1HNpBEEQBEHwhMVioVmzZu7P8TM5LwKWsmaggIAAEbAIgiAIQiPjSXcO0elWEARBEIQGTwQsgiAIgiA0eCJgEQRBEAShwRMBiyAIgiAIDZ4IWARBEARBaPBEwCIIgiAIQoMnAhZBEARBEBq8cwpYXnnlFSRJ4uGHHwYgJyeHBx54gHbt2uHt7U3z5s158MEHyc/PP2M+uq7z3HPPER0djbe3N4MGDWLfvn3nUjRBEARBEM4jZx2wbNiwgQ8//JBOnTq5j6WmppKamsp///tftm/fzqxZs1i8eDG33377GfN67bXXePvtt/nggw/4+++/8fX1ZciQIVit1rMtniAIgiAI5xFJ13W9uhcVFhbStWtX3nvvPaZOnUqXLl2YPn16pWnnzZvHTTfdRFFREQbDqRPr6rpOTEwMjz76KI899hgA+fn5REZGMmvWLMaOHVtleSwWC4GBgeTn54uZbgVBEAShkajO5/dZ1bBMnDiRYcOGMWjQoCrTlhWismAF4NChQ6SlpVXIKzAwkJ49e7J27dpKr7HZbFgslgoPQRAEQRDOX9VeS2jOnDls2rSJDRs2VJk2KyuLl156ibvuuuu0adLS0gCIjIyscDwyMtJ97mTTpk3jhRdeqEapBUEQBEFozKpVw3LkyBEeeughvvrqK7y8vM6Y1mKxMGzYMDp06MCUKVPOpYynmDx5Mvn5+e7HkSNHajR/QRAEQRBcdF0nJycHh8NRr+WoVg3Lxo0bycjIoGvXru5jqqqycuVKZsyYgc1mQ1EUCgoKGDp0KP7+/vz4448YjcbT5hkVFQVAeno60dHR7uPp6el06dKl0mvMZjNms7k6RRcEQRAEwQOqqjJnzhyys7MJDg4mLS2NoqIiBgwYwIABA+qtXNWqYRk4cCDbtm1jy5Yt7kf37t0ZN24cW7ZsQVEULBYLgwcPxmQysWDBgiprYlq0aEFUVBTLly93H7NYLPz999/07t377F6VIAiCIAgVzJgxg+7du2M2mxk5cqT7eEZGBjfeeCMxMTH4+vrStGlTFi5cSE5ODoqi0K1bN6ZPn86QIUPw8/PDz8+PoKCgOi9/tWpY/P39SUhIqHDM19eX0NBQEhIS3MFKcXExX375ZYUOseHh4SiKAkB8fDzTpk1j1KhR7nlcpk6dSps2bWjRogXPPvssMTExFd5QQRAEQRDOXkxMDM888wzLli3j6NGjZKZnc/jIIdauXUt+fj5jx44lMDCQ3bt388MPP7BmzRq6d+8OgKIoPPnkk/Xaf7TanW7PZNOmTfz9998AtG7dusK5Q4cOERcXB8CePXsqTCb3+OOPU1RUxF133UVeXh59+vRh8eLFVdbOCIIgCIJQNbvVSdfW/TiyO4fjOxZyKDmTb5//l9ywTUTFBfHoo4/SokULmjZtitFopGvXrmzdutUdsDQEZzUPS0Mj5mERBEEQhBNUp0b6IQtHdudwbHcu6YcsaJqOX4iZ33d9zb7DOxl30bP0vCGK7v07VLg2IyOD2NhYVq1a5Q5YQkND0TQNRVFo06YNzz77LFdeeeU5l7M6n981WsMiCIIgCELt01UVJAlJlsnPLCZ5azY5aUVYMkuwZFspzLGiqTpmXwNN2wXTd2xbmsYHExjuzaEXlnI01zUYxmFzVsjXbrczduxYxowZU6F25eabb+bSSy9l6NChfP/994wePZqVK1fSo0ePOnvNImARBEEQhEYi7/vvOf70MwAcjBtGdpcRFOQ5UYwywVE+BIZ507JLOAGhXkS1DCSsqR+SLJ2Sj9Hk6lNaUmR3H7Pb7Vx77bX4+Pgwc+bMCulbt26Nl5cXZrOZG2+8kfnz5/P999+LgEUQBEEQBNccKI5jxyj+ez0FS5dSuGIFSmAgan4+R5sOwFRUwtC7k2jeIRSjWfE4X6PZ9fGvqRrgClauu+467HY7P/30EyaT6YzXy/I5rZ18VkTAIgiCIAgNjD05mYIVK8j//nts+/aDJOHVoQPR06YROGI4zqISfn98PYlxJbRKivAoT6fT6X6gg8Npp6TEisPhYMyYMRQVFbFw4cJT5jlLSUlh//79xMbG4nA4+PHHH/npp5/4448/auOln5YIWARBEAShnulOJ8UbN7lqUVatxHE4BcloxG9Af8IfeQSfbt1QAgMBKD6exWcvbAXAL9zvlLx2f7sSS55K11v6YjCf+JifOnVqhWHJC1lIwpouBDSfzk8//YSXlxdhYWHu80899RRPPfUUhYWFfPfdd3z44YeYzWbatm3Lt99+S69evWrr7aiUGCUkCIIgCHVAVzXUPBuOtCLUQgeqxY4zrQhnTgm25GOUrHoT2Q/8BvTHr29ffHv1Qvb1PSUfzWbnm4nfk2dwrcHXPiwD3wATR444sRTJlJhCAIiMNtCkcxMkCeISw4hqGVghnxn3LKN5LzMjJvStsuwzZsygTZs2DBkypAbeiRPEKCFBEARBqEeqpvP7ymTC16YTlu9ACTSjWmxQVkUggexnxBjpi2QC2RxMyJ0PEnbXSCTp1E6y5clmE+M+voGP712ETTezKysCssDsKEAySDQ1HueYPYICi8SmxYcByE0r5oq7EytmJOmoTq0WXn3tEAGLIAiCcMGZMGECX3/9dYXOpUuXLnUvCTNjxgxmzZrFtm3buOKKK5g/f36F6wcMGMDatWsrrJW3fN0W9hYa2ZKSx597M7i8ECbihbGpH16tg1BCvDAEe2EI90YJMLtH71gPppL10QGU4OAqg5XyRtzZinkfHQVg9J3NiOrWBnD1fzkw9Apiv5jNsrVGjuzMoX3v6FMzkHQ0Z+NpZBEBiyAIgnBBuu+++5g+fXql506exr4yU/8zjT4jb+GvA1ms2pfF9V/uwSBLxEf7M7JLE5rvt2BJs9Ph/qRaKX9E17ZM/KDtGdN0HdycIztz8AupZOZ4CVRV1LAIgiAIQqN1zTXXALBly5YKAUuuNZepS5axKSWHrb/uJiBzHSG+JnrEBfPujV3p3y4cv9KOrks/3YKeZq80/7riG+Qa8WMvcZx6spHVsNT9QGpBEARBaABmz55NSEgIHTt25H//+x+aVnltg6qpzN8/n9t+u40+X13B93/5YHfq5K+dS/b749B/eJxL9J0M6xTtDlYAdP1El5X6YvZxNVnZStRTT0p6o6phEQGLIAiCcMF58MEH2bNnD5mZmXzyySe89dZbvPXWW+7zmq6x/vh6lqcsZ3Xqap5b8xyyJHNp9GgAggfcyIED28nJyuSVV17hgQce4Mcff6x4E01H87xLSq0om0zOXnxqDYskN64aFtEkJAiCIFxwunbt6t7u1asXTz75JLNnz2b07aP5+eDPLNi/gNSiVKxFVmL9Y/l19K/E+MWwbO8OFq5M5s1JlxEX3QyAIUOGcPfddzN37lxGjRrlzre6NSzFG45j2/st4Ipy7IeT0UpK8GpfbnFC2Ynil80pfXPL7at5+QAcP+5k8Sd/ArBpSQpZx4rQNR1d09E0HbUghay9e/jwyUXoug667n4u2y7bLy4qosjfpxqvpuaJgEUQBEG4oGl2O7bduyk5sJ/MPkP44SF/+ra/kqtbX838g/P51/YvMX4xAAR4uzqvFthsFfKodKp6XfcoYDFGBKNZ00HyQ8s+cVzyboviDY4sVy2JbHbNo1K0/Gt0e36l93Pn2bw5qm8wUAKA065y6N9MZFlCkiVkRUJTN6IVpWJVQgDJtZhiWSQky64YSHIdVyw5KJZcD15N7REBiyAIgnDec6SnowQGInu5Ao45s2fTNzgYVq9h7c8/8999exnbvBk+dvio55vExvfE6XTyvfo9mqZhtVqRZRl/sxeatZA1v29gXGJ3zGYzK1as4IMPPjhlwUDJw4BF8fOm+fRrq0xXsjOb7Nk7afHjdyj+Z17rp0zbgfGnPffti/PxDWrDsAf/r8p8Ppt0L4GBgVWmq00iYBEEQRAatYULF/LYY4+hqiqJiYnMmjXLPWuqQ9VYsOAvlFdfZNmhrXxjySfS25vkvDwcuo5JUYgODeXehx9m2+7NDPltJfKV19MhoROLFi1y38Pb2xtfX1+at4jlyK59fBvkww/T3gAgLi6ON954g+uuu65CuXQd9Brsw6KXTvImGWum+6liMKA6Kxk91ECJTreCcAHJTSvCYa9ktIAgNFKFhYXcfvvtzJ8/n3379hETE8NLL71ETpGdT1cfYsDrK3j073w+SbgKY0w0I9u0Ycktt3B01ucUHTpEkdPJ/vR0micmcigllV9btOSnuZ+xY8cOtm/f7u7Hoes6hYWFzP/5ZyRJYvwbr2KxWLBYLGzdupXbbrvtlLJJqk5NjsFxByyGmvnolhUF1emskbzqgghYBOEs6bqOU9MpVjXyHE4y7Q6OWe0kl9jYlV/Avv37Sd27G01tGAHC8QP5fD3lbz568M/6LopwAZMkiWeeeYakpCTatm3LV1995T7322+/0bVrVzp16kT//v3ZuXMnACtWrCAhIYHx48eTkJBAt27d2LJlCwCLFi0iKSmJ+HhX08fIGyfwwaez6fHyMl7+dRcXtQghGDth3gYCr74a/8suo+mbbxIy/mZMcXHue8+dO5eRQ/qhSBJBQYFcf/31fPPNNwBomkZ28h5+vW0o0666jJbhzQj1Ca76tZY4KTbUXBWL7tBcnWuVmslTMRgbzN8nT4gmIaFRcH3DsaNpDjTNjqbb0cu2NTu6XrZtg/xjBBTpGDCA01buYQXV7nouf8xpA9XGTaYBbA7qRLvAIByajl3XsGs6Dl13P9s0jRyH6xdc4swjAJqlHmLsgk8Y+fizNIlP4M/ZnzDgltsx+5y6mFldcNpc5U7o16RO75trzeWPI38Q4hVC/6b9qzX1uHB+kiSJzZs3c/DgQbp3784ll1yCj48PN954IytWrCAxMZGvvvqKa6+9lh07dgCwY8cO3nrrLWbPns23337L2LFj2bVrFykpKcTGxrI5JZf3Vhxg2bYUCnOz+O/lrRndI5YwPzNX/bMd79JPu3nz5vHHH38QGhrKs88+y6WXXgpASkoKoy/t4rrXM49g9w5h056D/Jz9L4E7jxKebqUFsOLgcf4vIhzfbXugim4nGhUG75wz3akhGeQa+x2SDQa04sIayasuiIBFcNN1HYeq41A1HKqGzWHD35gPnAgGKjzrpcFCadDgPqc7XMGE+9kVaFQ85kDTbFgs/+J0WjCZIjCZQk4EIO7rSq/VPZ8tsuu/+Rjyy7fLSmDwAoP5xEMxg9HL9WzwQjOYWBbeFXSIMhsxShJmWcIoSRhlCbMsY5BgcVa+O2AZFh7IoNAAzLKMUZIwyRI/pGWxICMPTVJI3PUPLXr0xnuJTta8Tei5xawP+o6+N9xSs/9wHmrWIYSJH1xWp/dMsaQw7Mdh7v23Ln2Ly5rXbRmEhueOO+4AoGXLlvTr14+VK1cSHBxMYmIiiYmuBfrGjRvHxIkTOXbsGODqJzJw4EAAxowZw1133cWRI0c4nF3E77sz+O29v2gV7svTwzpw9xtwe9+WGAyujzirJuFlULjnnnt4+umnMRqNrFmzhlGjRrFhwwZiY2MBKOsi22JHDgG5B/CxluDvY8AJHEgIYavRREleDj39g/jhoJ0Zv+9zD10uG6Cjo6Pp4FQ1mhTbifUgZNFVncLVx9B1HekMtSe2A/lQQ81BUNaHpfE0CYmApQbpmmvlS9Wp4Ugvxuxvci1uVTruXVd1UDV0TXcdU3VQXedcz9pJ+6c+F+fncSx7LyW+RahOJ6rTgeZ0lm47eT29OYccPlzaLrxC8FG2bS/dd5btO0+cc2qn1hfcGD+Pgc1XefDqJWTZVPowI0lGZNmIJJmQZSOyZEQq/yybUBRfvL2bUVCwg4CATnh5RSNLJqSyfEqvlWQTsmR05+/Kp2y7LK3rvCQZcW7vR37TIAJvXu0KVGQDp05aUJEMtPxtKQdN4VgO/8P1XS/juN3ByAhXte8Du1LYXlhCtuPEL7dV0xkbHVohH2VrV64Dsr6JZFfMYLYWOtCdO+lJGyIObCDxxfdPWwZndjZZ776LT+/e+A8adF7URJQPVgAWHlwoAhbhFGf1s67rTHzjO9Yll0BGKp+P68rQjlHs3r2L6Ohod7ACYNUlvI0KUVFR7mOXXHIJSUlJ/PPPP8TGxtK8eXMycyyAq2bk+OV96RATx4DXXndf892ECdx6ay+UhYtRHU4+W5Nc+qdFco3+LU0nSxIGReJOXSHAdGJxxNOxHcojf9Eh13tROtHb6ZhiAzx6ezwhApbzkN3q5LeZO7AW2ksDkhOBSdm+5tDQyn3gXx1U9Q9plSRAllwRd+nYea3Yicmus9nxJ7LBiGIwoBgMyKXPh+1tQQKjIuNjljEpMkZFwqjIpY8T2ybDiX2DImOqkE7mye/+xC53pWvSfeWCEVdA4g4eygIHydBgPmAtigl0Dcz+1bouXsvlIOEs82rFsp2uJdmf25/qPn9lWCC/Zp2Y+2Byy2jeS8lga24KvyUvw2zw4V1vsGuw68psMg8dJDy/JX7r51CQcgRvoOC3xRhjYihcswZ7cjLRL7yAqXlzdE0j439vkP/DD+R+/Q2+/foS/uBDeCd0rJH3pL78fNV3/N/CcbQzhbLXK5ZVW4OIW/wLAK+N7sSYHs3quYRCffjss8+YMmUKycnJrFq1iunTp+Pr68u2bdvYvn07CQkJzJkzh4CAAAYOHEhhYSGpqaksXLiQfj0vYtrjkzDpKvsMzRjZpxlfL32bcf06EBsby9GjRwkPD69wPxsyqqOYsLAw+vTp4+6gu2bNGlJSUlyddHNy+O2339h8RX/+9+7HLOk7gIULn3PnYbFY+O6779i8eTPa8t/5v0GteOWOy8/4OvMWHMB2MK/K90P2dn1ehN7UHu+EsOq/oWdJNhhFwHK+Kci2krIjmxadw/AL9kIxSCgGGdkgu7cVg4xidD3LigTf7cUR6k30yFbuoENS5IoByMnPsgSKXHH/JNumzceQZ+KONz6ptKy/vzaXjdk6H40fVul5Ty39dzWaBk78CA6+6Jzyqmu6YgC1+r+Evf2N/OqAD0Is3JPj+hbTJ8iPKLOR0ZHB9Aryo8XKre70AzfsKd0yoHglYEdjVsklpOZtJMMpQ8hmljgSyEs54r4m/T/TKtzzwOAhmFq2RM3PR83OJvSO2/Hq3JnM//6P5Guvxe/SS4l47FHMrVpV/42oR1888xdBkT4MuakZ8w7tA/bRznoPNk7MHRHub66/Agr1SlVVkpKSKCoq4u233yautPPrV199xfjx43E6nQQEBFBUVMSSJUtIS0vjmmuu4YF77sZWWIjBaOCtaf/hqRQv2kaaGTFiBL/99huFhYX06dOHzz//3H2vLl26YL3oXt5e8Q0O1cSyZcvo0qULiqIwa9Ysrr3W1RGluLiYkJAQ1mzeQ/9LBzFp0iR38xTAnDlz6NatG23atGGvoqB7sgaPLLlqyKsglXbMlT2cW6WmKAYDmghYzk9dh8QS1dKziXOS5+3BHuaNV5uqe5LXJIMs4cHvR5Wm/noAiz2M1pFnrp5siHTFgGy3njmR6oTsfeAogV//D479gyl6BLR9lKlFoYCDTn7efNO5FcZygeO/F3dkSXY+mXYnK3MKiPU280zLKHZlWvgr9S++2rWZssF3Exw9iH78caIffxxHegZaUSHOzCxkX19kby8KV65CzclGK7Ei+/vh378/3l26AOB/2WVYfl1E5ltvcXD4CPz69iX8kYfxij/9JFANhaZqWLKsWLKsLPlKIjnxO2ZsKHAHK6/0eYFwn2x6Nv+9nksq1CRJknj66af55ZdfKCoq4vnnn2fcuHGAa/TP5MmTcZZ+OA4bNoyXXnqJFStWcP/99/Pbb7+xadMmzGYzn376KV26dGHevHl88sknxMfHs2zOlxicdnJzS/jls4/pMeIa8gtLmPzftQT5mmjXrh2RkZFMnz79lHJt2bKFQcNuJTUwEp82vWh69C+2bPn7lHQ///wz8fHx7tFHJ7vrrru46667XDuKgu7Jl6KqeuaX0ktjn8q+pNYmxWBAO4svd/VFBCy1qu4XlVIUiZoYpGaUNYa2SeP+IbfXQG4uuq4DGrquAVppM5LnAZHDkYfNnlnau8313uropb/teum2jkMvxtdmh8NrQXOAWvawgcMKjmLY8DGkb6+QfyfFNYX1UZsDGfhffLMKwQpApNnIzTGuKttJca728N05u3lz45vsz9tfIe2lSaPd28bICCACc8uW7mNnqjWRFIXA4VfhP2Qw+fPnk/3xJxwaOQrf/v0Iu/NOfLp39/h9q2uyInPPuwPYtyGd6FaBXJ7vS6fcjSz9+Q+uObQG77UmciY3wdu7eX0XVTgHM2bMYNasWWzbto0rrrgCqDj6JzExkWeffZa0tDRsNhtvvvkmDz74IJIk0bt3b3x9fVFVFavVys6dO7n//vvp06cPY8eOZebMmTz22GMcP36ciNBQmvp6ocgyBUUl9LzmegwGA7nHXXPYB3ibOMbpR/8cOnSI/IztHFmylFY3vYB+mk6wn3zyCbff7tnfO0lRwMMaFo8+Bsq+ZdZDwCKahM43DaNrhkcUSUargamKjIqOrRo/x1lZv7N338tomhVNs6GqVkA9KUip+JtrNIbS55JVyLJnTQN/r78Km+14lenaSIWEW6zw2dDTpJDA6A2tB8HA512jhnzCSPINZY/DiUMHL1nCz+BZMPXahtdOCVYAmjc99/4nsslE8JgxBI0ahWXRIrJnzuTwTTfj0707IXfcjl+/fkiVrWFSj/LSjuO022ibFM2+Pn3RCgoIB24sPa+nl9BrwOYG0+dJODsxMTE888wzLFu2jKNHjwInRv8sW7YMWZa59dZb6dy5M6+88gpXXXUV4PriEhQUxPbt29m/fz/jx4/n+PHjjB07losvvpg777yTESNG0KtXLzo0i6FrkDefrt/G5Ben8sgjj7jvbykoBsCm6hVG/8xduJTBw6+h2a1vovqEkT7nKUbd+X/Ikuv3RLVXXAMI4PDhw6xevZo5c+Z49NolD2tYJAmPmoTqK2CRRcBy/pFKIxa9GhUmuvt/dcsggxOFf4/klQ6108sNudPdQ/DUstU6dd21reuoGu7tYoeZEns2KSmfoKOB7go6Kt1GIyVlJiATF3sPsuyFrJhdNSjIrgW1kJEkGUqfs7J+JyNzEU5nISaTZwGL05lPWNhA4mLvwRVFli7W5Y4oS/eT7DiLHRhMQa4RQooJFKPr2ejjClBO82EZaKz+r8SMy2bwwYZ3+G7HN1y8zUF8y55cff3ThEXGVTuv05GMRgJHjCDgqqso/OMPsj78iKP33IupZUtCbrmFwKtHuNdIqU+aqvLJQ3cCcJ1vBFpBAaa4OJTQUEo2bsR/8GCinnu2QZRVODfXXHMN4GpyKQtYwNU/5bnnniMxMZG4uDgkScJkMtGyXO1ieYWFhbRp04aLL77YfSwvLw/fzKPsSM3gf3O+IeOLb1i3bp179E/6oQMsfOVZaD6e51bn8U/BcUJ8s9mXUcCa/XYM4S0oPLoXrzgfHBnJzHv9MX743//htJWQ47DRt2VLvh0/3v03+u0VfzC4RUtsb07neFntrXuccrkll0uPqXl54MmEa5KEVmAn64udp+Th2nY9aSWuoEGq4rtHQU4Wi999A0fZwov6iaHYrtWVT2SaftD1JSoirpXrC2PZukblZu7NOXYEk7d31a+jgRABS3VUJ2KpJ34mmRLZxNXvrjnHnIJoFbiTg4cWln4TlkubbyQkqeI2yJjN0QQFdqdly4c9yt1kCicjc5FrojcPGQwB+Pt1JDCwa9WJ67DrkI/Rh6tfXMEVyTZCbruNyIerXkjsbEmyjP/AgfhddhklmzeT89ks0qZMIePVV/Ht0wff3r3w7toNc6sWSErVw7lro3wDxt+B0+EgqlsvDlw+GHtyMvqRI4Q98ADh991b7ZqVsg8OUSPT8H322WeMGTOG9PR0LBYLkydPRtM0cnJyWLduHb169WLOnDk0adKEJk2asH//fnJzc7n++utB1/luxvNEeTu5MtabzGIrGSV2jlqdLFu2jISEBMaOHQvAoc3/4K8W8UDEMf4wtGfb3oMYA8JwqjqtTBYKCo6y+tUJtG3bFvvr13LjzHU4NZ0Nv36L4d/f+LxLB6xbtwGuIcxzNvzDK926Yd292/VCyo1Rdn8hKl21GMDcvj0+F/Ws8v3waheMI7UQnGWdVMr9DEtlDwnZ14hP1wgMIWcOHrJTDpOyfStte/XB6OVV+jshnci63Be4/PQ0rEWFRLeNLzf0unQ1Zsk1DLtFl26ENY+r8nU0FCJg8UBJoWvSMku2lejWVacvqwI05lgp+PPIiR/S0h+a8j//Zb8Y+RlpFOblun6Y5NIfKqXcD5eM63jRmYOmy0JKsK7+kX4330F481h8g4JLb3FingBJklBkCUWSkGVQZAlZcj3KH/czX4kkvXw2b1mVnKprdkVX01HVVLUYmy2tWgFOXVILCpC8vZG9vcn64AP3txjLwl9A0/AfMgRUJ7qquaqSnSq6poKqojtV0FzPuqqeSGcrgtSt6E4naBq6xok5fHRK5/dx3V8rLqZgyRIKlixxl6nNjQ4M8RdDeHvXz1lADJj8IKYLhFT+bfdcSZJEt2EjAdx//Is6dOTxYWP4YOwIIqoIOg7kHeD7fd+TWpjKxC4TUWSFq+dfzdWtrmZqn6m1Umah5qiqytVXXw24JoVbsWIFAJdffjnDhg2jSZMmBAcHM2/ePCRJYuu//yIBObtXk9jUD5Nu5ctrgsnItzLux6NYSqwMGTIEH7MZqbRPDMCmX3/ijSWr+PGuB3l0YF9uueUWNm7ciMFgQFEUPvzgPdq2bQuAySDz3b2u2ptZvvuYbzxMyx9+cJd5yZIlmDZv4qaVf9Z4UGyOC8R8a82tcFz213/A+DvwDz3z8OfL77q/xu7bUIiAxQOO0inNSwo8m201P7OEXKdOWJ4Nyx9HKa23O9FOVK6msazWRnOqeGMoDSxO90ujYyaMPEPWae9tSTtGmCOHnZ++RqvuvRj5f894VOa6ZsnfDIDJFFpFSpfMzGWu9ObwKlLWD9+ePSlau5bcb75xfxOTJAlnZiYAloULQVGQFAXJoIBicPU9MRhcHfgUGUkp3TYoSLKCpFmRnDlIwc3AaHadk2UkRXZdq8iu/BSldHi8jO7QKEnJwhQRgNK9A6RvgeSPXcsP2AtOFLhpD+g8FjrfAKYzLxVQUlJCYmIiWVlZ5OXlAbBz504eeOAB98iOESNGMH36dHx8fEhJSaFDhw4nMjh6hJIF85k0/0oW/rzgtPdZdXQV9y2/z72/PGW5e7t1kAffFIR69+ijjzJ69GiSkpJ46623CAtzfaj+97//5YYbbmDr1hPTAjjT9vPrx/8hwAxzh1jg7pHQ8252/Pg/Rj++gKmTn6JLi2asmv8d3/y9hSbhYQQGBnJg43pKCixMGtyXpD79ACoMYz6TCRMmMGHChArHBg8ezKFDh2rk9dc23T2cqH7LUV9EwOKBoEgfAMKaeTYRmabqrC1SuebezkS3DvLomo/uu5WO/S+j93U3omsaqlMFVUNTNXRNQ1dd39jRNKIC/U6bz+V3TqTP2PH89v50nJV0LmswJAlv7ziMxjN/+9i97x3GzG3KkI5LGRoITZvcVEcFrJ4mb/yv5jPdvxy+XAQPr4KgGphgTXWCzQIHV8C/c+DXx+HP12DoNEgYfdrLnnvuOWJjY8nKOhEo33jjjVx88cUsWrSI/Px8rrrqKl566SWmTZtG8+bNKSw8sT6J3W4nJiaGG28Ye9p7bM7Y7A5Wrmt7HZc0uYTVx1bze8rv5Fhz6BXT69xfv1An2rVrh1cVfZS+/O8/pO08zh+7s2kSFQmPHwTZ1cl918FJNA2QeOyll3FYrYQ2acq+9GdYutwVwG5Y8L07n22/LyFp6PALprmwrPZeqqqzS+2VoJ7u6yICFg/IpT23PertXV41fokKsjNZ98NcfIKC6dD30rNeIM/k7YPJ2wefwCBKCguqvqC+6Dp2eyZbt008Y7K0HetY4simj+Uz7kxogiRdQD+yZd+m5BqaC0cxgE8IJFzjeuQehqXPwne3gWyEDiNOuWTjxo0sXryY//3vf4wZM8Z9/ODBg7z33nuYTCbCw8MZMWIEa9euPVF0Xefw4cPs37+f1atXo2mau5NmZdoEtcEoG3FoDubtnce8vfMAaB/SnhxrDvm2/NNeK9QPp9PpfmiaRklJCbIsYzKZuOmmm3j11Vfp2rUrkiTx6quvcvXVV1OSdYT2/93K/+V5s3H/7wT5+LH/z29P/IwXZdGpeRCpBTq39ulGh5goim12Nh4+RnxiJwCufuxpDm7awN8/zuWPWR/RqltPAiMi6/GdqEsXdn+uhjUesoGSqhmw6OcQhf7+6QfsXXeuHWZdnR/dH3gNUGhof4ICu6GpxWd8dDT6ECXlsm/8ANq2efrC+kXVSkch1Na3qeBYuO5zaH05R+dNxpG8vsJpp9PJnXfeybvvvovJVHEGzscee4zZs2dTUlJCWloaP/74I8OHD3cVW9OYN28es2bNYvXq1Xz55Zf06dMHs/n0o8H8TH5sunkTf9/4N7OvmO0+nm3N5j99/kPP6Ko7OApntmDBArp06YKvry8xMTF88MEHZGRkMG7cOJo2bUpAQABJSUksWFCx2W716tX06tWLwMBAmjRp4u5IO3XqVLy9vXn55Zf5+eef8fb2ZvDgwQBMnz6dmJgYWrRoQbt27WjevDn3HD3CyudvAGCZt521uxdxTccY5KWufinkHITXW9G2eAMzrw5kZfJxpixYztsrN9Bj0BBmfTMXAG//ADr2H8jgux8EQHU6uFCUfQZpmupeP67iw+F+aJ6MYqqGhvC39wL6unr2ympYNE9rWEqTVeffV5JkdF2jy5BhxHX2YBRMlfl5NiV0fQkJuYSQkEuqTtgFGH2BNtnqZQFLLc42LEkw9ms+nvofmPUrjz3WAT8/V5Pj66+/TlJSEv369XN3nixzxRVXcOutt+Lv74+qqowcOZLbbrsNwD0RmKIo3HTTTbz00ksMGjSIH3/8kaFDh+Lj43Pa4vgYfUiKSGLbLdtwaA6Mcg2sySWwePFi7rvvPr788kv69u2LxWIhPT2dwsJCkpKSePXVV4mJieGXX35h7NixbNiwgQ4dOrg70T722GOsWbOGI0eOcOmllxIXF8eUKVOYMmVKpffz9fVl1qxZ7v2lr7/MioI0muw3cUuvDRwyhvDjvP+S9Mc4OH4Mdv4E344HILv5CNJ25fLoFYHuD0lJkvjumUmubdcB9/whNf3B3JDJiutvwUf3TqgyrSTL3PrmBwRHxdTY/et7oKwIWDxQ3RqWs+EXGkrH/gO5ZEwN9dGQpBPzCAiNU1kNS001CVXii1cncbzEBLj6HLz77rsMGTIEX19fPvjgAzZv3nzKNbm5uQwaNIgXX3yRe++9l6KiIh544AFuuukm5s6di4+PD35+fhQWFjJr1iySkpK48847WbJkCUePHmX8+PEEBQVVWTYRrNScZ599lueee44BAwYAEBwcTHCwa+z/Y4895k43fPhw2rVrx7p16+jQoQP5+fnk5ORwyy23oCgKcXFxDBo0iG3btnl03/yMdD5+4MTssYVNInnq7rsw++rwZrmO2aXBCjfMwTemD/3ClqA6HOXmGCkb3q6X+9DUMXl5ExLT9CzekcapeWIXhj30OKrjzLVKOalHWT9/HsV5eTUasNQ3EbB44EQflmpeWI/VApIknehRLjROJxYYqbVbHCnxItbHRlyTYNr2G83K9f8yf/58Dh8+TFpamntoqMPhoKCggLCwMH755RdKSkrc06ybTCbuvvtu9/TsJSUlFBYW0qJFC5577jkmT55Mt27daNGiBbNnz+bLL79k6NCheHl5kZWVRVZWFlu3biUqKorIyEjCw8Np06YN3o1oQquGrKioiI0bN3LllVfStm1bLBYLffv25e233yY6OrpC2oyMDHbt2kWnTq7+IiEhIdx222188sknPPnkk6SkpLBs2TLee++9Su918Gg+OxcfQXE42bmi4ghF36Bgbvrf+5hLa/AY+Bwsf9G1HdYO7lkNBhNeQI8Rp+8EfiEzGI3EX9yvynTZx46wfv68OihR3Tqnv4SvvPIKkiTx8MMPu4999NFHDBgwgICAACRJcg+DPJMpU6a4J7Qpe8Q3oEXeyj4vPG0SaggVG5IkN+gmIcEDRa4h0bU9+VvL5k24ZNwThDdrzejRoxk/fjzdu3dn4sSJPPjgg3z77bd89NFH+Pv7s2XLFtq3b4+fnx/vvfceTqeTgoICZs6cSVJSEunp6cyePRtJkjCbzWRlZXHDDa5+CyEhIYwbNw5Zlvnyyy/5+OOPmT9/Ptu2bcPb25sDBw6wZcsWfvjhB2bOnImjim+Rgmdyc3PRdZ358+ezdOlS9u/fj9ls5qabKtbm2u12xo4dy5gxY+hebq2qMWPG8NFHH+Ht7U3r1q256qqrGDr0xLIXuq6y49gvPPb7g/z26koO/5PBgS3Z7vOxnbpxzZNTuPv9z/HyKzfCse+jcMdyCI+H9leBoW5XKr4QnEt/ysozbKSjhDZs2MCHH37ojsTLFBcXM3ToUIYOHcrkyZM9zq9jx44sW7bsRMEMDafy52ybhKR6rGKRZFHD0ujtW+p6ru3VVE8KiFq2bMl9993H9u3bWb9+PStXriQ9PR1VVSkqKiIwMJAFCxbw5JNP8vTTT6MoCt27d+f222/nww8/JDg4mFtvvZXHHnuMa6+9lsDAE0PXw8PDuffee0lPT0eSJPz9/U/p0/Lnn3/yxx9/YLFYCA31bJ4e4fTK+iQ9+OCDxMbGAvDCCy/Qpk0bioqK8PX1xW63c+211+Lj48PMmTPd1+7Zs4err76aL7/8kpEjR5KZmcnNN9/Mk08+yauvvkpKyqccPfYlJSWHuQIovGI9Rxf8x1Xz5n8TIU3CuGbyQHct9SmadoeJp66cLAiVOauooLCwkHHjxjFz5kymTq04+2RZbcvJnfSqLIjBQFRU1NkUp9bJskRM6moy//srBb6ut0yidF4UHVfVfekkcLquodpVWudo6PYEIMCje2iqyrrv55CXdvy0fU+q6qVd/vzxfXvw8vds3hihgWpzOez7DT4aALJ80rebU9cjAcDoBTfOPeeZbCVJIjExkcTERNLS0tiwYQMtWrTgm2++cae57rrrGDVqFAUFruHzx44dY8CAAVx88cUYDAa+/fbb0+Zd9ruu6zo7duxg3rx5dO3aldjYWPeKu570c7lQaKqKpqrkpqWSumcXx/ftIS89Fb/gUALCI4hs2ZqWrZthdORBdOcKQWhQUBDNm1e+Mrau69jtdq677jrsdjs//fRThRFh27Zto2nTplx77bUAREdHc8stt/Cf/zzHkKE/oeuOCouXFphMHG7lTeyBEq55fAhN2tbhGhnCee+sApaJEycybNgwBg0adErAcrb27dtHTEwMXl5e9O7d2z0BVWVsNhs224lJ0SwWS42U4XQUo0y7fXNxmv1QjT7okuz6jChbx8E95b6rOcvHko6/04p33u1AhEf3SBhwOal7dlKUl1tpC0ClMcxJB8tX//mFhtKy60WevUChYep4DRRmgFbWNFJ+TQdOPWYrhL/fh6z91QtYqgiEo6KiGD58OFdddRV5eXls3LiR1atXExUVRXh4OEFBQQQHB9OkSRP3h53VauXAgQPk5+fTtm1bHA4Hy5cvZ/9+14Jsfn5+BAUF4XA4SE9PB2DTpk1s2rQJgISEBBSlFkdHNQKazYZ1xw62LvmVNZvXuY9Lskx4bAtCYppSlJdL2oG9bFjwPVc3201rv0wIag7tR0CrS10/B4HNueuuu3jnnXcYOnQoISEhvPjiiwwcOBCz2cx1112HpaCQR17/hPUpFooL7LQo0mgW7k2HFlGkpqYyf/58hg+/inmbvuGJN/+Pjk2L0HXX37ay5TJywu7lb+UaxveIpEfg2c0jJdSQ0s+G/T//RPaaNSBLaPkWnBnpmNu2K5embOb1igsnopf9ZdHdHZ6tuTlotvqdjLTaAcucOXPYtGkTGzZsqLFC9OzZk1mzZtGuXTuOHz/OCy+8QN++fdm+fTv+ldQSTJs2jRdeeKHG7l8VqXShqGZPTiJ47PVVprfu2cuhq6/G5OV5F6E+Y28+lyIK5yPfULjsac/TF2a6AhatOn0/dDztHa5pGlarlcTERBISEtA0DV3X0TQNTdM4fvw4hYWFHDlyhC1btmC1utaJWlK6vlHZNO3t27cnMjKS5ORkfH19GTx4MFu2bKkw8mTLli1cqmx0zco7/C0IbVWN19Q4ObOyKF6/nuKNmyjZutW1FpPDwZrOrtfeukdvul45gsiWrTF5VeyQnJd2HNvbvSiRA/FuPQi2fgtrZ7hOykaeDIojp4mVzh3aADJ9O/fm7Zue5/8e+JCffvoJyWDir97tATAgcX/vm3ig9834yXaeejyCx5+4iRvGleAwSPh19GPEHaeuY3Ndp8e4rlbfIcFTck4esqax8Z+/Tj15YOfZZ7xn79lfWwOqFbAcOXKEhx56iKVLl1Y59XJ1lI0uAOjUqRM9e/YkNjaWb7/9lttvv/2U9JMnT2bSpEnufYvFQrNmNTB1eQ2RFFegoque9SHRdR0tPx85MLDKZh9BOC2ldBiwWvOdVQsKCpg1axbZ2dlVpg0MDKRTp0707t2bt956y3187PXXExZe+VpQu0sXSrzyyiv59ddfXX1Xfn/AdfKdrjBpNwREV3ptY2N1WjEpJmyqjS0ZW/gr9S/ss+YwcplrOQNTbCzeXToTePXVGFq2hLdeJjIiisCISGzFxRzZse2kVXddz4GKhrdmgdhLIOFasByDvMOQk4ySc5D/XVbI/3q4fkaKVW8K7L5c1rkXB55YxPu4akT2oFIogzEuAGPHAhw/m7g64mZ6v/81eU6JKcddgdJMq86blLhfU2JC5aOGhPrh4+fP5duTiXrlP3h16QK6jlZYiDM3F1N0TLnvKFK5VajLVsflpGOuxIdvvpmQTvXbbaNaAcvGjRvJyMiga9cTE5upqsrKlSuZMWMGNputRqpxg4KCaNu2rbv6+GRms/mMs2bWO7m0ZuWkTq9aURH2lBTsycnYDh3CnpyMPfkw1tIFwQKuvLJ21qQRLgxlAYtWnU66UpXj9XVd5+eff8ZqtTJ+/HiMRiOSJCHLsvtRFmh7e3u7O3kCdLE72GIycvWP88mcMxfr0KFETp6MMbJiU+mVV17J0KFD3XN9+Pn5weY819IBAO/2hHtWuWbnbaR0XafT7E6nHA/1CuXWlCKyWobQa9aPGCNOvDf2rCwMTpX0jDTSf5nPxl/mnzb/sbEQ7AN8f+qXvJOphBB5fxIjgK5PrnIf7xjpj7llIJJJgdwgHKZj+Pl2oNi3HV7OAl66aDzPrn+bezrfR/+EW7Baj2EwBuBlbpj9Dy9USnAQiq5j8vHFLzauRvL09vOv9y/U1QpYBg4ceMqEQbfeeivx8fE88cQTNdbmXFhYyIEDB7j55gbUTCJJeLzwU+k/av6Cn8lf8HNpYJKMs7StHkAJDsYUF4e5VSt3wGI/drSmSy00dvuWQXE2roZkrVy7cyXbZYHKuvfhwO+lP4dy6bcp+cS3prLjSICB1NRU/v3pPcp+vss6fZf9P7PAwd4DFq7pEkrL3JXQdij4e/YBdZEE7ebMde8XLF5MweLFBI0ZQ9RzzyKVjgaUJIniNcdd3wT/SScns4QcOYnw61dh/rEv2PLho/4w+hNoPfDc3tM6pus6/6T/w8fbPq5w/N7O9zI4djCtglrx5zudCMvK4feZz9ProZcI9HM1uciSxMCdhwmZ9jJeF12EuXRElV7awd/Vv6C007+jGL1wH5JvCJj8XD8XmuqaMVlT3T8j2d/sRInvQf6SZExNKja5O9OLcaYXo4R6gabjHR9GxKVdaRF9gzvNyPZ3urf9/NrW0rsmnAt3YNEQ5tioQdUKWPz9/UlISKhwzNfXl9DQUPfxtLQ00tLS3LUj27Ztw9/fn+bNmxMSEgK4Ap9Ro0Zx//33A66ZFocPH05sbCypqak8//zzKIrinr+hsVGCgpCMRvIXLMAUG4upRRyBXZMwx8VhiovDFBuLUm4EhHfXJNKefQ4l4MwrFwsXGEsqfOXJBFrlqm8Bjv3jenggkPFsyw9l2+aMM6Yz4iBuy6uwpQD6PV5l3xpd1ylavYbiDSfKEb99G7Z9+8j+9Avyvv0W24F8Ip95FiXIi4LfUyjZllUxE81BybezMRtBDYhHiWgKX14DzXrChF9O1Cg1UJqm8ceRFXy8bSbbs7fTOqg10y+dzsDmpwZcJWH+kJVL8y9WsDr1Doa9Ox8A3ami6DoBwSH4xTTx4K5xVaawOUFbmwvkVjgeM6U3qsWOIdTL3awtNE7ukabnWReDGp/s5IMPPqjQIbZfP9esfJ999hkTJkwA4MCBAxWWqj969Cg33HAD2dnZhIeH06dPH9atW0f4adq7GzpDcDBtN6xHMplcixBWIfi668j5/HPMLc9tKKpwnrGVrrY9/ido3psT7c0yFduey9HUE1P6u75+l9bAVPbQuUfTUL2C3euzuJ4qf5bll+G/7UA+9c+GVlxM5jszsPzyC0pwMFpBAY7UVEwtWxJ2372ETZyIpCh4tW+PVuKqJbBu+4vM91a41kqSFCSDF5LRi7A7EzFF+2L7dTbe211zgjh9e8KQySj74+HI3/BSGAx/B7qNr6E3u4boOkdX/MmeBUvZW9KfowGHKOxdyHsD36NPkz4VqtQzizNZnLwYp+Zk0/1daRPUhoyPP2Lc8j2suX8cl8z4Cq3I1a9FrcGRkD5J4dgPWUAC74QwlGAvvDuEICkyslfDmf9KEE52zj+dJ8+3cqYFscokJydX2J8zZ865FqP2VTNSLVyxAmd2tnt+lhPV9/qJ6LfcOTUvX0z0JlTkKO3U6BUIBg/7bMlKtdYeUkofntIK88ib9wOW9ze7fo41Dev27e7zwTfeiFZSgj0lhajnn8O3b99TgvaISbdQtOondFsBRUsrTt/uN+wtsmaWNTvH4qtMxEtej1fqF0jvfl6xMD8/ABtmwq2LwOxHrSnMhI2zQLVD5i4oyYOUdaeOxpIUMPnR1JZP00Do4L2MH3L+Q+SSB1j/Ry4bYr7FHAoT77ye97a8x/v/vl/h8hXH/sSrq8S4FRCybBO6puE4fhwA28GDNfZygq4UX4wuFCVbtoCuu34HZQVkyb0tyRLIMkpAAF4dOlSZV0Mgwunq8LA90Lp3L8cefgQAyWSq+E24rGd/ue2yh7l169opt9A4OUvnPDDU3Ii8c+a0oatgatXK9cdPkrFu344SHEzsV19hbtmiyizMLZvR5q9VFK3bjG6zU7L5H3K/+BivxCTC7+7hGl2n6uiqhu5MRFN1CrNSUXL+wvvIa0hdbz4xZDdtK0xrAqGt4bYlrqHgNaEwA7bOhYN/wuG/wFFU9TW6itUcg9FegqLbiTbtpm/gx/zGaMwFASjJfpAMU/bPItOcw/jcl/DuXsz9t9yI1WlF1VUu+eYSwFVDduTee91Z+118cc28LuGCIHt5Ifv7k/PJpx6lb718GcYmVTU51n9/GBGw1ALJ6GpbD390EmF33llFakE4DWdpDUtDCVh0Hdmg49/KQOj/vew+HP3Si9XOSvH1ImBgb9eOVkjuF9DkzdcwNT3dzKhRQFeYUVrL0qI/5ByCCT/DW50hez+83tI1pLfT9cyQLGzM2MRHl3+EsTp9XTJ2wdp3XfOYSDI07wn9HoWuE2DzbFg2BcLbu2pahr/tmo141Ruumh5AK8hG0e3u7IKbhvF/D7hmid2wfRvrZ2QSUBhGeL5rUsywVNdKul6l/8Z/3fAXqTuewfHTIrTCIiRZxu/SSzG3FZ1bBc/J3t60WbMa3WqF0vmSUFV0rayDvoaualh37ODYQw+hFRd7lG+jGiV0QavGP5RSunaK6JMinJOyGhbjSasW24vg06GuGU2TbobWg0Cp/V9l3WFFAnRTSI3mK5eOfJE9mdvJ5Av2QrhlwYljU/Lh0Eo4/i8kr4afH+TDFq6AYPeWz0lMuu3EVAOVKcyAvYth+/euier8o+HSydBtAniXC6CMvqCYYeK6itd3GgOH/gRJwZhxkHWF4yjWAukwejBNe/d3J+uRkEiPD1zbyVuz+OW9rVx1X+cKWfkYfWj96hvw6htVvxeCcAayyQSmMy8oqWa7+pI2loVyRcBSG0qDG11Vq0goCGdQ1odl3ftg9MFdJZu939UUkrYVdi8E3wjofD10vhEia68tWrcWIwFqUGKN5lsWsHj0Lc/k5wrYTtain+tx8QNwdCOvr38Te8ZOEn9+FMcf31Ac+X8Y2nfBGOmLZFbQCqyomxbhnf0RUtoWQILYi2HUR9BxVOUrB1vzQLWB017xfLOL4H7XzN+Lpm/iSHoeAAe+V9DmreKmF3ohGyS8/VzXHNycyd71aQAoBjEaR6hHZf3dGkn/SRGwVMPpFiU8mVQ2H00jiVqFBio4DgKbu/pSuEmuzp4hrWDCQijKgi1fux5/vQNNusO4eeBTs7UgAHpJ6ailk2t8zpHs48pPKympIiWnD1jKa9qNoU2/dFV9H/0H61c/UrDDF3buO6kZvjkRca0xjbzbVUvlV8W6X2mu+ZIoyoTAytv7hz+YhLXIwaf/txp7iWtenFlPrgFg1KNdiWkTxJrv92HJci1bYDRd2OslCfWsbBVtTQQsF66yqFUTNSzCOYjpAo9sO3OagBiI7gSXvwj/fAKLn3TN31KbAYvJx6P0jmPHKFixAkkxIBkNoChIBiOSwbUvGQygGLAfTgZAK6q6hkU3+qJlHkXKyXI18ygKkmIo3XY9u78wSBI064HU6ihsVIl5tjfOXAe6Q0XLSCX7hyy0jjdDlwGevQGJY2DXz2dsHpZkCS8/I0mXNycvo5jIFq7V2tfNP+gOYAZO6MCP/3Ut8qgYRQ2LUH/KRvB5uoxMfRMBi4eq09WoumsJCcI5M5ggsqNr28OAorr0YlfAIpk8q2HJ+fIrcj77zBVMePANrqyz+pkU5PTEkjIBXtt1hlQqoAFl+ZUu1GctwNTEta16FQNZZP9SgrzoR7yNGwny+apcMCJV3AbXsGYAp/XMr0OSuHj0iRF/GYctrJt/kG1/HiM3rdjjmlpBqHWnWUamoRIBi4d0HUo27SLPewllf8zK/uxIFf64ge5wfZPSnaKGRahDZU0lJg/nJNF12DbP1elUc5Y+yqZyL7df+ixnHgYg+8tvUP5JLx2OT+kCfOWm/pddC/IVb9yEsUkTWi9f5hqd4HSil384nKC6tiWDAWNMTJVF1qL6wKFsQvoXuoIgTXd1GNT0E6MfSrfVIoXio/74NMvDEGxEDjmxwrASFU1wj79R861YMwKxOvvBxSG424zK5k2i7Kl02xwAQXGevb+lHFbX34Hj+/NIO5jvPh4Q1kBGfwkXLsnzJqGGEGiLgMVDksEXy8LvsCz8zuNrnDkiYBHqkDtg8fUsff5R+OFOV4dexeSawdb9kE/aV5CRsVojUKUQHDt3uic91MsmRiwNGKD0mKbj07MnUFr1bDK55iU6B5K3D0pQIT5X9PUo/ZkWu/AdPRIA7deDWHfmQN9JZ0h99pq0C2biB5fVSt6CcC7Kmk89HyUkhjU3Cr6DXsQYYyDg0malX7j00tW4dfc2Ou5z2bN24ZXYq17LLFxg7IWABAYPO8WWlK4lc8tCaNqtyuQS4AXU53rJkiKjO2u2+lqSpUYzrFMQapQkmoTOS7K3D7494/BJqnoBMl3XyZ2XiVTP0ahwgbEXuWpXPFi/CgBb6fo0Xo1n0U3JIKE7azi4kCVQRcAiXHikRjZKSHRR91RpDYpHSUvb9htCm59wASkLWDxlLQtYAmqnPLVBkaGGO7NLiiw6yAsXJjFK6HwlVW8pBQmP1x4ShBphLwRrPnwx6sQqzWVNluWV7Rdlup7NjSdgkQyuJiHrvlwMwV4Ywk5t/lItNtQCh6vzr1LaCVgu/1z6zVKR3N8wdVHDIlyIxCih85QE1YpYpGoGOIJwrtoOhdxkTgzJlU60UVeYO6R0O7AJdBwJxsYzWkUJNIEOWZ+4Voj2mdAanDrB7SJxZJSQt/AA9kOW6mcsZpwVLkDueVgaSZOQCFg8Vd0ARELMdCvUrdiLXY/zmHf7UKKf7UXOnN3Y9uWxObUPAO2+nFUhXeAVcZhbBrk606onhj67h0CX29ZVHUOwuR5ejSDULycwd+z1sHQppl9+cR+3l47mMzid6IAuSWjdu3OJrnF5/RTVVZ56vHfjouvYLSUU5RZUevrUVSwlHA4HJeWmGy/fp6WybR8fH2RPO0wKwgVK8TUSNLwVx99Yj64acFqi3efMrYPw6xWNd0LYGXIQBAHA6e/v3u4W5vqdkSSJAlVDkSX8DQYkSUaSJf7MzMRWz6uGi4DFQyUOK/rqNEpWp3l8zZJly9i14qjH6bt168bw4cPPpniCcEExRvjQ/JUBNGcP+YuTsbcpIOjq1hgr6dMiCELlyr4gjx07lvj4+DOm3fXeeyi+1ejUXwtEwOKhpd7baOIbQXz7U/9RT9e3NiGmFwlGqULty8k1MWX7f/zxR4XaGEEQPBM4NK6+iyAIjZonI1o1TaukJaFuiYDFQwVmG8E9mtK2b1Kt5L927VoMBvHPIQiCINSNsgDEk4BF1/V6D1hEhwkPFRcXs3r16lrL3+l0oihiqXlBEAShblQnANF1vd77WIqv9NXQunXrqhOdJafTKWpYBEEQhDr314oN7PjnAJIsIUsykiQhyxKSLLv2ZYmiwmJUR/2OfBWfkB7y8fEhMjKy1vJXVVXUsAiCIAh1xmAwYrAHkHr8GKkcQ0cvN+eYKzjRpRPb+Ufrd74WEbB4qLbb7kQNiyAIglCXJCSCc7ow6NYOtL0osnSCbNdq67pG6bOOrsP8NzYR5RdUr+UVfVgaCFVVRcAiCIIg1KqFCxcSHx9PmzZtuO66aymxFwG4m4EUg4zBqPDytJdo0iyanhf3oNclPZjx3fPuPN5++20SEhJITEykU6dOfPnll6fcp6SkhA4dOtClS5caK7v4hPRQdMxfZGb9yC+/milfXQY6kqS7dstVnem6RG5OP0pKWpTun3nSuMLCQtEkJAiCINSawsJCbr/9dv7880/i4+O5796JLP7rS0bIPSpNP27cOKZPnw7A3JfXu4937NiRNWvWEBgYyJEjR0hKSqJ37960atXKneaJJ57gkksuYcOGDTVWflHD4qHo6EP4+8nIUhiyHI4sR5Y+YpCkpkhyUyQpFkmKQ5Ja4OV1nNDQTCIiIoiMjCQqKoqoqCiio6OJiYkhJiaGJk2a0LRpU5o2bUqPHj3o0KFDfb9MQRAEoYGQJIlnnnmGpKQk2rZty1dffeU+99tvv9G1a1c6depE//792blzJwArVqwgISGB8ePHk5CQQLdu3diyZQsAixYtIikpyT1J3N133c3GA78jUXWXh6wjhRzdkwvAwIEDCQwMBKBZs2ZERUVx5MgRd9ply5Zx7Ngxxo0bVyPvQxlRw+Iho9FIq5bjiIu716P0f629jNjmrWnd+upaLpkgCIJwvpIkic2bN3Pw4EG6d+/OJZdcgo+PDzfeeCMrVqwgMTGRr776imuvvZYdO3YAsGPHDt566y1mz57Nt99+y9ixY9m1axcpKSnExsa6845tHkd+cQ5O1VnpvefNm8cff/xBaGgoib7DaMup85AtW7aM3NxcevRw1dLk5eXx+OOPs3jxYncQVVNEwFItng/pkiQZncaxAqYgCILQMN1xxx0AtGzZkn79+rFy5UqCg4NJTEwkMTERcDXdTJw4kWPHjgEQGxfHPb/bcCz9hXD/EPYfPkqXx78hd8debFnH2DptORIg22wALPpsJ4tn70EDdAkcmoa1pAP3Xf0ZsmLg8PHtfPrL8zz3xBcVyrZt2zZuvfVW5s6di2/ptP33338/Tz31FBERESJgaSwkyYCuq/VdDEEQBOE84smIVV0Hh6pjkCVu7hXLDoPCyKQmHPbqwLrFO7muezPQdY4f3s/2oDCkTsGggaTrSBrsPJxLUHA4ESG+SOi0je1F0MZWbMjZDVwFwM6dO7nqqqv49NNP6dOnj/veq1evZvXq1Tz22GNYrVZycnJo164de/bsOefXLgKWatCrVcNiRNMctVgaQRAE4Xz32WefMWXKFJKTk1m1ahXTp0/H19eXbdu2sX37dhISEpgzZw5NmjShSZMm7N+/n5TDyUQe3spbk8ahHF5P8ybRTLmhH4WFXWn14ctc2UwjPj6e++9/hztuu5lHJnY/5b5Hjx6ladOmAOzbt4/JLx4ktF07AHbt2sWVV17JRx99xOWXX17huuTkZPf2ihUrePjhh919aM6VCFg8Vr15WGTZiK7Za6ksgiAIwoVAVVWSkpIoKiri7bffJi4uDoCvvvqK8ePH43Q6CQ4OZt68ee7al44dO5K8fRmP3fA5EYG+fPPNN0iShL+/Px9//DEjR47E6XSSkJDA559/7r5Xly5d+PXXX4mJieHpp59m48aNGAwGFEUh4dpHCG3iuveDDz5Ifn4+TzzxBE888QQAr776KkOGDKnV90LSPVn1qIGzWCwEBgaSn59PQEBArdxj5aqLaNZ0PC1a3O9R+o0bx+Ll3YSOHf5XK+URBEEQzm+SJJGbm0tQUJDH15TVahRe+R+eH96Bm3vHnTH9m0v38vnaZAK9jRhkCVmSGNO9GXf0bVGh+emqd1bRuWkQL49KPMtXU7nqfH6LGhYPVXemW0k2kpY2n8LCvQQFdiutoJFwzS0ogVRu23WDcvvlz3PS/snnXdvu4+59QFLw9+9IcFCvel9lUxAEQag7iiyhalXXR7y1fB8AQztG4e9lYOaqQ7z86y5W7svk/Zu64WduOGFCwylJo+B5ZVRY6KXk5v5FYeFO3BPN6XppP5iyyeX0E+fQT+xXSHfqdSefL59P+W1Nc6BpJQQF9SS2+Z34+bXDyyumJt4IQRAEoZadTQPIgAED2LJlC+2eWUReiYMimxNFlpAkUCQJgyJjd2pouqtTbpdmQWw5koef2cDTpjnc5vUVva1vs2pfFt+sT6Fv6zA0Xcdq16jv9hgRsNSS5s1vo3nz2+q1DLquk529gj17n+ffrXfg59uOnj1/rdcyCYIgCLXP26Qwfdk+pi/bd9o0/mYDAd5GAJbtSudp36VEk4WMhobMy7/sqpDez6t+Z2MXAYvHGl+TiiRJhIVdSlBQd/7dejc2W3p9F0kQBEGoA1/e3pPk7CI0HTRNR9N1Jn37r/t897gg/knOo8DmpGNMAJ/d2gPJfy3jP/kbbV8WABMujsPfy4AiSUxfvo82kf719XKAc5ya/5VXXkGSJB5++GH3sY8++ogBAwYQEBCAJEnk5eV5lNe7775LXFwcXl5e9OzZk/Xr11d9UR1rrL2TDQZ//P07IMvG+i6KIAiCUAcSmgRyVacYRnSOYWRSE67p2pTkV4ZxaNqV7Hv5CpKzit1pd6RauOjl5UxbtIvHhrRzH/9mfQrz/jlKod01E66q1e9kqGcdsGzYsIEPP/yQTp06VTheXFzM0KFDeeqppzzOa+7cuUyaNInnn3+eTZs20blzZ4YMGUJGRsbZFq8WNL4alvJ0XUUSS0cJgiBc0CRJwqjIZBWeOu3Gh38eZMSMNYCr064iS6RZrHy86hAAx3KtdVrWk51Vk1BhYSHjxo1j5syZTJ06tcK5stqWFStWeJzfG2+8wZ133smtt94KwAcffMAvv/zCp59+ypNPPnk2RawljbWOpTRgkUQLoCAIggD+XgYKrE4WPtCHEF8TRkWmwOqg2K5ic2rER/njazawaPtxVuzOZO4/R2jbGJuEJk6cyLBhwxg0aNA5F8But7Nx48YKecmyzKBBg1i7dm2l19hsNiwWS4WHcGa6roIkalgEQRAEuPGi5gBc9c5qkrOLCPc30zLcj4QmgXSLDca3dDjzFQnRvHptJzpEB1RrtvfaUO2v3HPmzGHTpk1s2LChRgqQlZWFqqpERkZWOB4ZGcnu3bsrvWbatGm88MILNXJ/Tx21RLDDYiHg2K+Ajq65BxLjHqZcrnmv7B9WRz8xGtk99Lj0kH4iNUDnGJXowNJM9HL5lv8hqfS4zonxZnol6aGwcDeSVL89vAVBEISG4Ymh8XQP8+fOH/7lx3UpWI8X47RrBIZ5o0gSyCBLoMiuL7pWu7NxDWs+cuQIDz30EEuXLsXLy6u2ylSlyZMnM2nSJPe+xWKhWbNmtXrPN/65gcxif05tFiqbsO3cXRS1kbs7fV7uiOR+PjHxW/nn0mnlpNMdlyocj4ocXiPlFARBEBq3bSuOsvfbfQT7S8zbdpx5245XeU28qaAOSnZ61QpYNm7cSEZGBl27dnUfU1WVlStXMmPGDGw2G4pSvW/xYWFhKIpCenrFIbfp6elERUVVeo3ZbMZsNlfrPudMCWV8rzAeuiwWWT4RoEjSidlqJUlyBwlSuUChNEAtNxst7uYZuTT9XV9sxMt4JQMve65OXo4gCIJw4bIWOfD2N/LjXUmkFdrIzi7BWuwgINwbvWwodFlrgq6zaUkKF4UE12uZqxWwDBw4kG3btlU4duuttxIfH88TTzxR7WAFwGQy0a1bN5YvX87IkSMB0DSN5cuXc//9nq3bUxeMskygjw+hASG1kr9BlvFgFmVBEARBOGeaU8PoZSCuTTBxHqQvWp2Bt1K//SCrFbD4+/uTkJBQ4Zivry+hoaHu42lpaaSlpbF//34Atm3bhr+/P82bNyckxPVhP3DgQEaNGuUOSCZNmsQtt9xC9+7dueiii5g+fTpFRUXuUUMNgSRJtdp+J0kSmohYBEEQhDqgOnUUpZrdGep5do8aH+f6wQcfVOgQ269fPwA+++wzJkyYAMCBAwfIyspyp7n++uvJzMzkueeeIy0tjS5durB48eJTOuLWJ0kCrRYjFlkChwhYBEEQhDqgOjVkQ+MaOXrOAcvJ861MmTKFKVOmnPGa5OTkU47df//9DaoJ6GSyJNVqk01t5y8IgiAIZTSnhlKNgEXX672CRUx96ilJolbHoMty7dbgCIIgCEIZVT2LJqF6JqY+9ZAE1ZroVtV0iu1ONA1U3bXwlKbpqLqOqunu42rpolSFNrXex7gLgiAIFwZd05Hk6vZhqd8ARwQsHpIlqVr1KxM+W8+qfVlVJyxnUPuI6hVKEARBEM7CSmkRSwJ+5NWPKwYhkl5ZUCJBBIzSbqQfbeumgJUQAYunJNdYdE+l5pUwMD6C0d2aIkuuRaRkCWRZQnHvS6ULTLlGCbUK86vFFyAIgiAILvlRxyjOsHCF9zWYJS90qayPSunnXPm4RYI/ihZTFJN+akZ1SAQsHpKgWp1idR1aRfhxZWJ0rZVJEARBEM5GmvMYAItKfsAoGwn3DqdndE9GtRlFh9AOmJWKk7OO+XkLRq/6Xd5FBCwequ48LDr13twnCIIgCJX64PIPmLpuKh1CO+Br9CXFksLcPXP5cf+PXNPmGl64uOJ6fZIkoZVfMK8eiIDFQ3I1Rwlpul5uen5BEARBaDhCvEJ4Y8Ab7v1d2btYkryEjJIMRrcZfUp6vQF8polhzR6SqGYNi+4KcgRBEAShIbOpNsYsHENGSQYAv6f8XmmfTamemw1EwOKh6v47aboumoQEQRCEBs+smHlv4Hvu/UWHFtVjaU5PBCzVUJ2J3Vw1LCJiEQRBEBq+Pk36oEgKD3d9mN+u/e2U2pTanDjVU6IPi4fk6na61fV6rz4TBEEQLgx/pPzBx9s/BnD3NZGQ3J9D5fufSJKEhIQsyRhlI1klWVhVK6quklVy+vnD6rsPiwhYPFTdqfm1BrDugiAIgnBhWJO6hq2ZWxnZeiTg+tJc/jOrrE+Kzonjmqbh1J3sSd0DwMBmA+nbpG/dFrwaRMDiIUnijDUs/2Za2J1d5EqLRLFTbQAVaIIgCMKFwKk5SQxL5KVLXqr2tbcsuoVNGZsYGz+WXjG9TpuuvpuFRMDioe3HLOxMtfDyqMRKz4967y+0ErXCsZ02a10UTRAEQbjAOTQHGcUZ1b5uSfISNmVsAuDfzH9JKUihdXBrAk2BHMw7SJRvFCFeIezO2U1iWOWff3VFBCzVoOnQ6qUlJw6Ua/PRSlQ6JIQzsXcLdHQe3X2EDu3D676QgiAIwgVnwYEFACR+nsjPI38mLjDOo+s2Z2x2b8/YMuOMaXfn7D7r8tUEEbB4KObiaFJSC0gM8HEdOKlmTJLh6QFt6BkdBMATxzNQZDEISxAEQah9rQJbcSD/AABf7PyCZ3s/69F1T1z0BI90fYSD+QfJteVisVlItiRT5CiiqX9TsoqzKHAU8OWuL2kbXH8LH4IIWDzmF+PLje3Cea1dM4/S6+hi4jhBEAShTrQNbkuIdwhTL5lKkDmoWteaDCbiQ+PPmGZD2gaMsvEcSnjuRMBSS6ozBFoQBEEQzoWqqxgkAzF+MbWSf313uAURsHisupUlVk3n5YPHWZVbAJxoQSoLZNz77me9wvnyTkmru9JfFOjHc61r54dTEARBaDyWHF5yxvMPPPAA8+fPJz8/H39/f6677jpee+010tLS6NChQ4W0VquVK6+8kgULXP1inn32WZZ9sYz5R+eTeX8m06dPr62XcUYiYKklD8VGsKPQNUqobP64sqDnlGf3xD4Vj5d3ch7fpuWSYrWLgEUQBEGo0n333ccrr7yCr68vWVlZ7oDlmWeeobCw0J3ObrcTExPD2LFj3cdat25Nx/EdyV+ZXx9FdxMBSy15OC6qVvNvYjbxbVpOrd5DEARBaBwua3YZDs1x2vPt27d3b+u6jizL7Nu375R08+fPR9M0rrnmGvexW265hfk/zce20Vazha4mMYylGuq/BU8QBEEQTiVLcpX9TF555RX8/PyIiIjg33//5YEHHjglzSeffMK4cePw8vI6NYN6HkgiAhYPZdmd5Dqc9V0MN7FMkSAIglCepmtnPP/kk09SWFjIzp07ueeee4iKcrUEHM46jFN1cvjwYZYtW8Ydd9xxyrV6AxhJIpqEPHTM5uBYZv22352s/n98BEEQhIZgWcoyAC755hL8Tf70b9qf+5Pux9/kf0ra9u3bk2HMoNeVvZg1dxaPrH+EWD0WfYVOly5d6Ny58ynXiFFCjcxFgb71XQQ3UcEiCIIglAkwBWCxW/Ax+JBVksXXu7/m691fkxCawH/6/AezwczKoyvZn7efQ/mHWHJ8CRmpGTyy/hEAUtVUti/czuibR9fzKzk9EbB4qIu/D219KmnTEwRBEIR6dkmTS8guyeaTIZ8A8MG/H/DVrq/Ynr2dYXOHYdlgIaBrALKPjP2oncwFmYS2DyVcDSfKK4qh3kOZUDCBf9v+y4DPBgCuEawSErpTJ1vPRrNqqKqK1WpFURSMxrqdSE4ELB7qlvEXHXdtZM0280lnTq0mkyqrOqt0gpXKqtj0U/LUT6lPkWhpdzDUpzXQ8QylFgRBEC4EiqSwPm09r294nZva38Q9ne/hns73sDZ1LXf9chcFfxdg+d6C6lCJjIikc4fO9BvSj9dvex2AMWPG0KVvF3qE9kDXdfdD0zVWfrKSA6td0/7vWL6DGTNmcMsttzBr1qw6fY2S3hB60pwji8VCYGAg+fn5BAQE1Mo9jr7Th+C8A+Sagk85p1faPnPqwVMDj9OkkyoNeSqkji06TIE5BP/Jh05XZEEQBOEC8fOBn/l8x+ekFKQQYArg9f6vkxSRBIBDdQ13NionakSeePsJjBiZ+uBUVFXF4XBUGBlUUlLCjh072LlzJ8nJyWiaxpAhQ+jdu3eNlrs6n9+ihsVDTU0KdLke3+HT67soLn++hv+Gj+u7FIIgCEIDMLzVcIa3Gs6O7B08s/oZxi8az4hWI3i0+6OEeIWceoEDDAUGZs2axbFjx3A4HCQmJtKvXz8WL17MgQMHkCSJ2NhYBg8eTLNmzYiJqd+JSkXA4rEGVhElSVDFEDZBEAThwtIxtCPfj/ieJ1Y+wYIDC0ixpHBvl3tpGdiSSJ9IJEli9bHVHJeO05KWeHl5cemll7qOr17Ntm3b3HlNmjQJf/9TRxnVFxGweErXG9bkJ5IsAhZBEITz1M5Vf3Bw0wYUgwHFYEBWSp8NBmRFcT1k17Mky8iKQlznroQ1i0WWZF7v/zoXx1zMy3+/zN1L7wbg4a4PE+ETwXN/PcclnS/hif5P4G3wdt+za9euHD58mMDAQCIiItzLxjQUImDxmE6DGkwsAhZBEITz1pYlv2DJSCcoKhrNqaI6nahOB5qqomkqmrP0WVXRVRVrUSH//PwDwyc9hW9QMLIiMzh8AINHXkaaLZOHVz7C9E3TARjRagRTLp6CUa44ysdsNtO2bdt6eLWeEQGLp3REDYsgCIJQJ5w2G216XszA2+71KP3816dy4J91zHnu/yo9rydlQjS83vc1hra8oiaLWmdEwOKp9G1gbjhtea6ApYH1qxEEQRBqhNNuw2A6eRqN0xt89wPs7piIrBgIadIUTVVRnQ5Uh+sRu/Nvtv75Bwf+/BzbjP6YfXxqsfS1QwQs1ZHyV32XoBzR6VYQBKGx27RoAVkpyUiSjCRLIMlIkkRhbi5Gs+eTlfoEBNL1yqtPe75930sxF+ns/XsN8156mjHP/weTl/dp0zdEImDxlH8MtBlU36U4QdSwCIIgNHqrv5mNl78/PgFB6LrmWmRQ0wiKiKRJuw41eq/hkyaTkXyQuVOe4Oc3X2Hk/z2LYmg8YcA5rdb8yiuvIEkSDz/8sPuY1Wpl4sSJhIaG4ufnx+jRo0lPTz9jPhMmTHBNAVzuMXTo0HMpWs0z+4O5dialOyuSDLpa36UQBEEQzpKuaThsVnpdM5abpr3Jza+8xfhX32b86zMY//oMYjt1qfF7RsS1ZMSjT5O8ZSPv3n4DJQWWGr9HbTnr0GrDhg18+OGHdOrUqcLxRx55hF9++YV58+YRGBjI/fffzzXXXMOaNWvOmN/QoUP57LPP3Ptms+dtd3VCV6vX6Xb793D839Jry9eElNuO7QPtzjIwEzUsgiAIjZrDbgPA5FW369TFJnbhhpde55tn/4+ty3+j58jr6vT+Z+usApbCwkLGjRvHzJkzmTp1qvt4fn4+n3zyCV9//TWXXXYZAJ999hnt27dn3bp19OrV67R5ms1moqKizqY4dUNTQVI8T7/kWbAXgU9oxeNlQU/2fvjrHTB4g8kHDF7QcRQMedmz/MXEcYIgCI2aw2oFwFgPfUli2ranbe++7Pv7r/M7YJk4cSLDhg1j0KBBFQKWjRs34nA4GDToRF+P+Ph4mjdvztq1a88YsKxYsYKIiAiCg4O57LLLmDp1KqGhoZWmtdls2Gw2977FUgdVWroGcjUCFl2HXvfCgCcrP1+QBvuWgL0YHEWw/EU4+Kfn+YthzYIgCI2a3VoCUK3OtTWpecdElq1dxfF9e4hu065eylAd1e7DMmfOHDZt2sS0adNOOZeWlobJZCIoKKjC8cjISNLS0k6b59ChQ5k9ezbLly/n1Vdf5c8//+SKK65AVSvvozFt2jQCAwPdj2bNmlX3ZVSfrrmChJriHwVdx0Ove6Dvo9D9NpCrkb+uNax5YQRBEIRqKathqesmoTJte/UB4OtnHkV1OuulDNVRrU/gI0eO8NBDD/HVV19VWNXxXI0dO5YRI0aQmJjIyJEjWbhwIRs2bGDFihWVpp88eTL5+fnux5EjR2qsLKdV3Sah6tKrOZOuanc1IwmCIAiNkqO0pcBYTwGLt38AQ+97BIAfpj2PvaS4XsrhqWoFLBs3biQjI4OuXbtiMBgwGAz8+eefvP322xgMBiIjI7Hb7eTl5VW4Lj09vVr9U1q2bElYWBj79++v9LzZbCYgIKDCo7Y5VTsFxZkU5x/BVnAcR2EGWlEWlORCSR5Y80sfFrAVnEVzTTXXKnJawdDAOiYLgiAIHjm+bw+bF/8MUK0J4mpax/4DGfPcf0jdu5uNv/5Ub+XwRLX6sAwcOLDCSo4At956K/Hx8TzxxBM0a9YMo9HI8uXLGT16NAB79uwhJSWF3r17e3yfo0ePkp2dTXR0dHWKV6vuCvFmQ9ZSmL+0wnFJ11EAWQeFsm0dQ4jCs9ZUPJ65pbo1LE6bqGERBEFopL6f9hy2oiL8w8LxCQys17I07ZCIT2Bggx/iXK2Axd/fn4SEhArHfH19CQ0NdR+//fbbmTRpEiEhIQQEBPDAAw/Qu3fvCh1u4+PjmTZtGqNGjaKwsJAXXniB0aNHExUVxYEDB3j88cdp3bo1Q4YMqYGXWDOO+Ucw0DuCwSEJqLqGpmuounpiGx1VU9HQUDWNj9JWsi8szvOABR1SN8H7l7hqWiT5NA/FdT73sKhhEQRBaIR0TcNWVMTld91Pp4E1O+fYhAkT+PrrrzGZTO5jS5cudVca+Pn5VUhvs9lo0awpd/foSNuelzBjxgxmzZrFtm3buOKKK5g/f36Nlu9c1PgUd2+++SayLDN69GhsNhtDhgzhvffeq5Bmz5495OfnA6AoClu3buXzzz8nLy+PmJgYBg8ezEsvvdSg5mKxSzrxsQO4svM9HqX/at5A15BlT3WbAEYfV02LrrqalCo89Ir7fhHQ3PNaK0EQBKFhcNrtQO2NDrrvvvuYPn16pecKCwsr7Hfs0IEWXjKJA4fQtH0CMbv28swzz7Bs2TKOHj1aK+U7W+ccsJzcMdbLy4t3332Xd99997TX6OUmPPP29ua3334712LUOptqw6zUYgDVpJvrIQiCIJzXHDbX6KCslGTSDuxDVhQUgwHZYEBRXM9Gs1etL1C4fv16du/Zw7jrR9D/ptsBuOaaawDYsmXL+RewXCjsqh2TYqo6YTk6YiZaQRAEoSLFaARg/U/fsf6n7ypNI0kyN70ynYi4ltXOf/bs2cyePZvo6Ghuu+02HnnkEeRKps345JNP6NGxPc1j4xrF6s0iYPGQqqkYJM/fLqk6HWgFQRCEC4bZx5d7PvyCEks+qqqiqU40p+tZdTrJSzvO8k/fd08sVx0PPvggr7/+OiEhIbw7+388+egzzFv3Oe+88A49OvR3pysqKmLOnDncf+3pV3huaETAUg2e1pgcLThKenE6fx//m/u63FfLpRIEQRAaG9+gYHyDgis9l+a3D6heHxc1P5+cb7/lW/9/CD4cwvCk60kzJxN0VTBb1+znvr8eYkOHLe70X3z2KQYJAi1ZFHvV4hxjNUgELJ6SPA9YSpyuqHhQrOdjhARBEAQBXH1cLD4OtiSvJ8yailExYzKaMBlMmBQzRqPZtW0wu5p6FJljN96I48BBbgE+GyRzvWUhAFLp/F5Wo8rUmWMZEN2CNp1H8OoLz9M5OpyoVk0Z8ciL9fhqPScCFg9JSBU6C5+Jj9HVFtg6qHVtFkkQBEE4D71x6EOWDUjlh5T/QIpn11zUWeOxA7DIYmF90wB8ig1kpxeR+UsmIQNDAJhr2kHBz9nw7loOZ+VyfY/OZB5KZf77o7nl+VVIkozT6XQ/NE3DarUiy3KFYdL1RQQsHpKQPK5hcWquNRkMsnh7BUEQhOo5rmfRzKcJj7W+H6fmwOF04NDs2J12nKoDu+bAqTlxqg50XUfXNIpDinmpw7+s+c9GMl46hCwpREZGMGzEQB6+9z4W533FwtQd6J3z2bwtg54XJTHuqdtY8s5cACyWfwkMTGLq1Km88MIL7rJ4e3vTv3//0y6VU5fEJ6qH7JqdDzd+yO79uwGQJRlZkpEkCZnSZ0lGQqJALQBg/979yMdcPbOlctPuS5JEREQETZo0qfsXIgiCIDRoRY4iLosbxGVJV1X/4psqPxxf0Jm9i69lESVwtz8hBpU2vUYT0sqX3bv+Dy+vGACmTJnClClTzr7wtUgELB5qrjXnuHacRVmL3DUteul/7m3pxLZBN7Bp5Sb2OPdUml9YWBj3339/3RReEARBaDSKHEX4Gn1rNM9I/+Z8f9160vL3cduv13DErtHjq56EKhrjIiMYaI6s0fvVBhGweGi4bThNmzblqqvOIuLFNVleWR+YpUuXsnfv3posniAIgnCeqI2ApUxUYBsWjNnEU8tvxFeR+fnYDt5OLaRvzm7iQ+Jr5Z41pVqrNV/IVFXFYDj7+E6SJGRZrnTyHkEQBEEA0HSNYmcxfia/qhOfJYNi5LXB83h+4FxWjl1LE78mvL7h9Vq7X00Rn54ecjqd5xSwlKfreoU+LYIgCIIAUOwoBk6MNq1tPiZ/boi/gfVp67E6rXVyz7MlAhYPOZ1OFKVmJtcRAYsgCIJQmUKHa3FCP2PN1bBkW3LZkbKn0qk58m35/LDvB7pGdMXLUDuLMdYU0YfFQ+faJFSeCFgEQRCEyhTYXAHL1MWvE8BHGCQjBgwYJAMG2YRRMmCUTBhlEybZhEk28m/uvxhkBYNkQJHL0howSApG2cgf2i8V7nGl8Vr8DAEYZSN/Fa4g05jJmwPerI+XWy0iYPGQqGERBEEQalugPYT26RcjBdnRFQ2rXoJTd6LixKk6UXHgwOHalxyuh9lBgBpCmByBEyeqrqI6ne7tcL0pmeYTKy//XrIITVJRJSdmpw8PBEymZVD1F1msayJg8ZCnfVhyc3MpLi4+Y5qioiIRsAiCIAin0K0G+h+8njFP9yC8mX+t3quk0M6nj62m5x0da/U+NUUELB7QNA1N06oMWBwOB++88w6aplWZZ2xsbE0VTxAEQThP2IodAJh9au/jWdd1ju7O5e8FB5ENEpFxAbV2r5okAhYPqKoKUGWTUFlgM2jQIFq3PvM6QoGBgTVWPkEQBOH8YCt2Le3i5Wuslfyzjhbwx5d7yEi2EN7cnxEPdiEgzLtW7lXTRMDigbKApaoalrJmnoCAAKKiomq9XIIgCML5xVbkQJIljOaa6TNZIe8SJwve/hcffyNXPdCZ5h1CGlX3BBGweMDpLF3MsIqApWxSOE9WdXakpmI7eAg1NxfQQXc9dF0HVUV3qnh1aI93YuI5l18QBEFoHKzFTsw+hloJJDb8cgiHTeWqyd3xC27YQ5grIwIWD5QFLFU1CZX9gFXVh8WRmsr+ywZWeV9zfDwt5//oYSkFQRCExs5W5KiV5iBd19mzLo3E/k0aZbACImDxiKdNQmU1LFUFLJrVBkD0yy/j168vsp8fyDJIEpLrRmS+8QaWpUvPueyCIAhC42ErrWGpaZasEqyFDqJbB9V43nVFBCweqE4NiyRJVTYJyWYTAIaoSAzh4ZXn5eWNXtKwp0kWBEEQapa1yIHZp+ZrWLb/eQzZINGkTVCN511XRMDigSNHjgCwY+1alMxMJFl2ByeyLLv2yx6AIzcXx/HjIEmuBxJQGsToOmp+vmvTbj/tPWVvL9TCQiy/LQFNpejvv0HV8L98ED69eiGbTLX7ogVBEIQ6Zyt24htkrrH8dE1n3U8H2LLsCL1HtcLk3Xg/9htvyeuQX1ERAOt27mTdzp1Vps+ePp39Bw5WmU46w8rNxpgY9OJijj30UIXjefPmEfPaqwSOGFFl/oIgCELjYit2EBrjW2P5rfhmDztXp3LJta3pPLBZjeVbH0TA4oGmBgNDFi0m8OGHUMLC0DXN9Sgd1aNrOrru2kfXaXbpZRhlGXTNPfJHKqttKe2YK5nM+HTretp7BlxxBb69e7t2DAZXPnY7+y7pc8aaGUEQBKHxKsq3k3bIwt7NxyguKcTsbcLsZQRdxsvHRECID2ZvE0Zj1R/fO1YdY+eqVC69OZ4Ol8TUQelrlwhYPKDb7QTl59Nm0KDT9jmpDUpQ0CnHJKMRzWarszIIgiAIdcfL10hmSgFLP9xTZVrfWBsTJl/h3nc6VWa/tBxrgRP/EC8sR6FD35jzIlgBEbB4RLe5ajQkc821K54tyWx2l0cQBEE4v1z/TA9sRU6+mP0lXiYfJBSaxjTDkWdCdap4B8nYbU72/1lI0WEzP370J5cMSyQkMpDv3l1BSboBzacEy1HX0OU+17Wp51dUc0TA4gHd7qrRkOq5o6uu6+gOh7s8giAIwvnFYFQwBCloBhvJaa4BH+YgjbHjx1ZId/CiZBZ9vIljmwKYt2kLOioSCpGdZC66vCc//28nplA7RlPNz5hbX0TA4oGyJpj6Dlgy/vtfdJuNzOlvkfv1NxgiI4n7+iskY+2sOSEIgiDUj/zS0aQA2dnZp5xv2SqOidPiyEzLZtvaQxTklNAiIYpOPV01KsXN5hLb+cxr2jU2ImDxgG6zIxmNZxzVU+tl0HW04mK8k5Lw6daVwtVrsG7bhmqxYAgNrbdyCYIgCDVv+PDhHD58GJPJxJo1azh06BAtWrQ4JV14VCiXjTr1MyCubQzHj6fWRVHrjAhYPKDbbOhmA5rTgaTUzhoPp9xT18HpRFdVdKcT+6Fk8r6ZA4B1zx704mJ32QRBEITzS0JCAgkJCWzYsIFio5l7V26g6b6j+BoUijWdDQ6Ik1Taepu4MrYJA+JODFkuLi4mPz+/ytnZG5vz69XUklzfHaS+kk/qynjQcM0Bp4GkcWK/XAzT1DGctiOnV+seuq5z6JrR2A8dQnc6oXR23ZMFjb0eU1wcWlERktGEQawKLQiCcN7q0aMH60z+zM6xcshhw+6UsBqM6F4SdnsJ6zQvZh/K5rHjGfSKiWDngYNkrVuDrutcd9119V38GiUCFg/IXZogHzPTrGg4Ohq6rqLrGugqGmrpfCuutOnS7xQWVT0crTK2PXvwHzwYn4t6IBkMSAYjktGApChgMKD4+eHTs6drXxAEQbgg+IWFYcw9xp7BPU85V+JwkLhiC/+1GuFgNkiBvN+tO5dfcjF+fn71UNraIwIWT5hkjF7BtB74apVJs7/phKZXXjtyMs1ux7ptG7rDAZqGpCj4dE0i5MYbz7XEgiAIwnmiWNXwVirviuBtNNLH18ji0t4BwYrEqCGD67B0dUcELB7QNDuy7NkIIQkZHdWjtLlff03GKxWDINm35qZkFgRBEBq/QnsRmuZk9t6lGABFAkXS2VNYyD9FMrKk4CVFY9UN5Ko6hfYS/Eze9V3sGicCFg9omg1Z9mzSOEmXwcOARbc7kP39afHD9yDJSEYDhoiIcyipIAiCcL5RCtdRqLXn8WMnz7Tu+rzopCTTUjrKES2EYnzIyNuEX8QldV/QWnZO43RfeeUVJEni4Ycfdh+zWq1MnDiR0NBQ/Pz8GD16NOnp6WfMR9d1nnvuOaKjo/H29mbQoEHs27fvXIpWozTd8xoWUNB1zwIWSVFA1zE1a4apaROMkZF1MgJJEARBaHjs9hwOHPgfhYUV+0EO997Dt97Ps/viNmzv3Zp/e7Xin54t+PuiFuy9pD1L+o3k9/4j2dq7BbO5ngD5/Bw9etYBy4YNG/jwww/p1KlTheOPPPIIP//8M/PmzePPP/8kNTWVa6655ox5vfbaa7z99tt88MEH/P333/j6+jJkyBCsVuvZFq9G2WwZqGqxR2kl3fMmIcloQFc9SysIgiCc39ZvGEHy4ff4e/2V7Nk7BYfDgtNZxNFjX+Ao3k6Q2ZcwLz8ivf1p6hNIrG8gAaYTtf+HU2ZiMAQQGJhUj6+i9pxVwFJYWMi4ceOYOXMmwcHB7uP5+fl88sknvPHGG1x22WV069aNzz77jL/++ot169ZVmpeu60yfPp1nnnmGq6++mk6dOjF79mxSU1OZP3/+Wb2ompaVtYzi4kMepZVQ0CUPgxBFOe3wZUEQBOHCYrMdR5bNtG49mePHf2DNX31ZveZiAPz9E894ra6rZGevICLiSozG4DOmbazOqg/LxIkTGTZsGIMGDWLq1Knu4xs3bsThcDBo0CD3sfj4eJo3b87atWvp1avXKXkdOnSItLS0CtcEBgbSs2dP1q5dy9ixY0+5xmazYSs3YZrFYjmbl+Exb+84HI4cj9JKyDgLCzhy9z3o6KDroJU+63rpcGhA13GmpbnmXBEEQRAuaKrqalGIb/cy0dGjCA3pR0rKxzidFlq1+j98fVud8XqbLR2r9RjhYQProrj1otoBy5w5c9i0aRMbNmw45VxaWhomk4mgoKAKxyMjI0lLS6s0v7LjkZGRHl8zbdo0XnjhheoW/ay5flDO/MNSxhTdBE22g8GAJOHqkyLJlO6ABFLpviE8nICrrqrdwguCIAgNnt2eBYDJFAaAn19bOnR4zePrJck1P5emO2q+cA1EtQKWI0eO8NBDD7F06VK8vLxqq0xVmjx5MpMmTXLvWywWmjVrdoYrzo2m2VEUz4YbmyKboAUpNLthRq2VRxAEQTi/FBW5Bpps+fc2DAZfJMmELBuRZRMlJSkE+HemR48fTnu9yRSBovhRUny4ropc56rVh2Xjxo1kZGTQtWtXDAYDBoOBP//8k7fffhuDwUBkZCR2u528vLwK16WnpxN1minky46fPJLoTNeYzWYCAgIqPGqTax4Wz1ZEVrUS8vL+PqWXtyAIgiCcjqa5ujl4ezXFYAhAkmRU1Yrd7lqp2VLwr3u7MqpahKaVuGtazkfVqmEZOHAg27Ztq3Ds1ltvJT4+nieeeIJmzZphNBpZvnw5o0ePBmDPnj2kpKTQu3fvSvNs0aIFUVFRLF++nC5dugCuGpO///6be++99yxeUs3TdQey5FnAYrUeA1ztiX5+7WqzWIIgCMJ5QlWLAOjV67dTptHIzl7Fln8n8M/GMfTo/iMARuOJL+rZOavZvfspJMlAcPDFdVfoOlatgMXf35+EhIQKx3x9fQkNDXUfv/3225k0aRIhISEEBATwwAMP0Lt37wodbuPj45k2bRqjRo1yz+MydepU2rRpQ4sWLXj22WeJiYlh5MiR5/4Ka4CuOZE8rGEJCOiC3ZZFaGi/Wi6VIAiC0JAV5mQjyTJGsxmD2Ywsn772w+7IRVH8Kp3zKzS0L/7+nSgo2MrKVa4hy5JkwGSKwGZLBSA4qBdJXb7Axye2dl5MA1DjM92++eabyLLM6NGjsdlsDBkyhPfee69Cmj179pCfn+/ef/zxxykqKuKuu+4iLy+PPn36sHjx4nrtJ1OerqtIkmdvla6rmMwnz0YoCIIgXEiSt2zk+2nPVzimGAwYzV4YzGZXEGNyBTJGkxm7ehTvqHDoX3l+XZO+YPOW8Xh7xyFJMgUFO9w1+gBJSV+e9xOPnnPAsmLFigr7Xl5evPvuu7z77runvUbX9Qr7kiTx4osv8uKLL55rcWqFrjs9Dlhk2YTu4eKHgiAIwvkpLz0NWVEY8ehTOGw2nDYbDrsNh9WK025zHbPbcFhdx49vTUM6JrN11eeYvP0we/tj9g7E5B1AUFhLZMWHHt1P7XS7/HfXCNbCoj34+8XX9cusU2ItIQ/Y7CUcPbqKY0cfAcpHsKVDlcu2AV1fAASdEsgBlUa/JpOJiy66CEU5fztKCYIgXGiKLXn4BATSqltPj9J//uJPZO0IYemMeadNk3BlW0KaBaEEH8Bg8CMk+GLi201lz94XOHbsa+LbNcwv/TVFBCweyMkOwmTejySlI0lltUMn1RKhu+MXiyWQ5EP/VJmvqqqUlJQQGxtLTExMLZRcEARBqA/F+fl4BwZ5nP7mp1dSkHcMu9WCrTgfW4kFu7WQLUt/JPNAHo4ihe2/7gWg63h/AqJz2bN3CgaDH5IkVWgeOl+JgMUDqamXExd3J1dccWWN5puens7777+Ppmk1mq8gCIJQv8pqWDwlKwYCQ0/tMNv+ojHu7ZQdm5j34nNEBd1D+679KSk5xvHj8yixHiEutmGMqq1NImDxgMPhwGj0bJRQdciyaxocEbAIgiCcX4rz8wkIq9kBGM07dqVV914s/WgGQZFRRLduR8uWD9foPRqys16t+UIiAhZBEAShOkos+fgEel7D4qkrH3iUsGbN+fWd/6JfYJ8doobFA6qq1kqn2LJOuCJgEQRBqB/7/v6LVd98jqwoKAYjisGAbDCglD7k0mOKwYDDZiOyRSsUkwmD0egalmwyYTCaXM8mEwaTGcVopCg/F++AoBovr9HsRVznbqz97mssWRkERlQ+I/z5SAQsHtA0zV0bUpNEDYsgCEL90DWN9Qu+Z//6vygpLKBDnwGoTieq04nmdJzYVp3Yim1kHTmMBBzftxun3Y7TYUd1nHmhwYDQsBot78HNG5j/2ksARLduR0BYRI3l3xiIgMUDuq7XyoQ8ImARBEGoH/kZ6az+5nN8g0No36c/l064q9p56JqG02F3BzBOe9nDhq5pRLZsUyNl1XWd+a+/xMFNGwBo2bUHI//vWaRa+CLdkImAxQPVqWFxqCr/HEvDoWnogKbrqLqO7n52HdN0nRKbjQz/IFQRsAiCINQph9212ODVjz5NdJuzW/fNNe2+F0Zz7c7KrqkqBzdtoNuwqxkw/s5avVdDJgIWD1SnhuWFtZv42FGNDrpdB9Bfk2l/lmUTBEEQqs9pcwUsBtOpa/c0NIrBQFjzOIrLLWlzIRIBiweqU8NS6FQxO3U+bBGOLEnIkoQEKKXbZQ9FkijQdG46mInvBdYOKQiCUB8KsrPIS3MtFph5+BDQOAIWgODoGIry8+q7GPVKBCweqG4fFlmHoa1bVJmuwKnCwUzUk2bNFQRBEGregv+9TNqBfe59WVHw8g+oxxJ5Rtd1co4dJbrN+b1WUFVEwFKFsoUaPa1hkasT2JQ+ayJeEQRBqHXWokISBw6hx4jRAJi9ffD286/nUp1Z9tEUln38HtlHU7j4uhvruzj1SgQsVSgbweNpDYsE6B7GLGXBjaaLiEUQBKEm2a0lbFjwParTiaIoyIqBEosF36AQgqMa/tptRXm5rP3uG7YuX0xQZDTXPjOV2MQu9V2seiUClipUt4ZF4uRlEU9PLg1s1OoXSxAEQTiD1N07Wff9HPzDwkEHTXWiGI1ExFXdXF/f8tLT+OqpR0DX6XvDLSRdMQJDLcy23tiIgKUKquoKJ5xOp0fpJUlCx7MqFqU0nSpqWARBEGqUtbgIgFtefxezj089l6Z6ti5bhLWwgHs+/ALfoOD6Lk6DIQKWKthsNixmH+Zu2ExOUGiV6dNsDsCzXudKaVwj+rAIgiDULHtxMUgSJq/anSOlNrRI6s6GBd+z7oe5XDrhTmS55peGaYxEwFIVo4k5Fw1Ck2Vmp+RVnd7gi5/d6lHWkiShSOAUNSyCIAg1ylZchNnbp1HOBtusQyKD7riPZR+/x5bfFnLTtOlEtmxd38WqdyJgqYJuMKDJMg+H+9HL17Oak7ggz4fJGSQJhwhYBEEQapStuBhTI2sKKq/z5VfiExTMgv++zA+vTOH2t2di8vKu72LVKxGwVKEslOgYEcaAiKAq06fbHCzMzEMrzEQvvVoHdP1EXnq5fDVd9GERBEGoabbiQsw+vvVdjHPSpkdvbpo2nbkvTGbxe28yYtJT9V2keiUCliqUhRKezq7yRWo2/01Oc+/7KLL72vLPZaOk/Q0yLb3N515QQRAEwc1WXNzoAxYAn8Agmid04uiuHfVdlHonApYq2Ep7xB63nXkZ8TLla0v8FZl9/TrVSrkEQRCEE0oKC9i6dBHgWnsn8/AhAsLC67lUZy/94H6WfPgOGckHANyT3V3IRMBShcLSYc1bC4s9Sj84LJCZRzMpVDUKVI1Mu4Nwkxg/LwiCUJsObdrA6jmz8fIPQHM60ZxOWvfoXd/FOivWokLmvjCZ4KgYhj/yJE3iO4rhzYiApUpBBtdbNDQs0KP0a3ILKFS1U64XBEEQao/TYQfgvplfVWvtt4aoIDsLh7WESyfcSdP2CfVdnAaj8Y33qmPFJcVk3TSC61vHuo/t3LmTgQMHEhwcTFRUFHfddRfFxa4aGFmSCDDIpF3ahbRLu2CUG/cvjiAIQmOgOp0oBkOjD1YA1ygNQDGI2vnyRMBShVdfeAElMrrCsRtvvJF27dqRnp7Otm3b+Pfff3nppZcASudVqY+SCoIgXLg0pxP5PPmAt1tdc3nJooa+AhGwnMHGjRv5Y8lv+IydUOH4wYMHuemmmzCZTISHhzNixAi2bdsGgCJJYjFDQRCEOlZWw3I+SP53EyZvb8KaxVad+AIiApbTcDqd3HnnnUyb/jbSSYtOPfbYY8yePZuSkhLS0tL48ccfGT58OOB6Q1URrwiCINQp1ek4LwIWTVXZvXoFrXv0Pi9eT00S78ZpvP766yQlJdGrb1/YdhBN1yguKcTH248rrriCW2+9FX9/f1RVZeTIkdx2222Aqw+L5vF6zYIgCEJNcDUJNa6PtLz0NFL37sJWVIhiMOLl58f+DevISz/OVY88Wd/Fa3Aa179uHdm/fz8ffPABmzdvRrFbALBq0HLdfppnH2D7hFv4z9Sp3HfffRQVFfHAAw9w0003MXfuXCTc/aUEQRCEOlJVk1B+RjoF2ZkYjCYiWrRCVupvQcFiSz4rPp/JrtUrAFCMRjSniq5r+AWH0P+m24hs0areytdQiYClEqtXryY9PZ22bduCruEsyge7Tt7Ifvi98B9KbDZSE5pjMpkwmUzcfffdXHHFFYBrFlsRrwiCINQt1elEkmRKCgtQFAVZMbhGDZUufvjti5OxZGYAMPC2e+kyZFidl1HXNLb/uYxVX81C13Uuv+t+2vXuh9nHB9XpxFZUiHdA4Pkx0qkWiIClEmPGjGHQoEGuHctx1j7fnzsWS+zYsROLLYeE53z44OcNPN69D2bFm5kzZ5KUlAS4ptwXAYsgCELdkmSZ3OPHeO/2Gyoel2Rkg4LqcNBn7HjWfvc1mqadJpfak3n4EMs+fo/Uvbto32cA/W++vcJkcIrBgE9gUJ2XqzERAUslfHx88Clb5TNPJ9xXQpIkmjZtyp/bDxH08lsUfvQWEZ/MwFeRuLT/AD7//HMAJI9XHRIEQRBqSq9R19OsQwKaU0V1OtBUFVV1lu47kSRo3+dSVs+ZzcFN61m+4HNKfHQkgwHJoCAbFWSDAdloRDEYUIxGDEZT6cOIwWTGaDRhMnlhNHthMrkeXiZvzGbXw9vsS4B/CJGhTdzl0lSVv+d/y7rv5xAUFcOY5/5Ds45iyZazIel64+9xYbFYCAwMJD8/n4CAgJrNPG0bfNAHgLwmzVF1ic3OJkgGhZ/9Lub7yMsZpOxGNZnRkDiqBrLbGcHS8E/OkGnFoCYq8irCwwfXbLkFQRCECnRNY97UZ8hNSyUnPwNHgAF8TeDU0FUVyamDqiGpOpIKsqYjqyBrEorm+ZfR7pPuon/PEaTt38uKLz4mdc9ueo66jl6jx4rJ4E5Snc9vUcNSBYdaSNmPl+ywIgM99X3IxQ66F25HVRS2R3RAcipIko4RO0NN23DYLVRshqw8LszJXUNu7l8iYBEEQahlkiwz5rn/kGPNof/c/ky/dDoDmw/06FpVdVJiK6bEVoTVVkyJtQirvQSbtRibrQSrvYRDB7Zh+eUfirJz+Hn6q+xdu4rQps257rmXadYhsZZf3flPBCxVcIY0ZXNSINHGzjS78udTzr9z2itv9ij/5b+3wuHIPevyCYIgCNVT5CgCwNfo6/E1imLAzycAP5/T1wL8blDY/Ms/7Pr8O3yDghlyz0N06H8Zslx/I5LOJ9WaOO7999+nU6dOBAQEEBAQQO/evVm0aJH7/IEDBxg1ahTh4eEEBAQwZswY0tPTz5jnlClTkCSpwiM+Pv7sXk0t0DQHBf5G/C5+rlbyDwnpS0BA51rJWxAEQThVscO19puvwfOAxZM8f09eDoBPcDBjX3ydhEsvF8FKDapWDUvTpk155ZVXaNOmDbqu8/nnn3P11VezefNm4uLiGDx4MJ07d+b3338H4Nlnn2X48OGsW7cOWT59bNSxY0eWLVt2olANaPIfXXcCIEu1UyajMRhNs9dK3oIgCI2JMycHx9Gj2PYfwLp7F47UVHwv6olkMmI/cgQ1Jxe/SweAJLmGK0sySKUjhOz5pLUIxBgQiFE2YpANGGRDpdu7cnYB/9/efYdHUa0PHP/O9k02vZCENFpC6D3SBZTeVGxYwN6woYKIAt4r6tVrF4GfV8GGCiooFnoRpEgLvQYChPSe7W1+fywEYhJSSEiA83mefTY7O3PmzE6y8+bMOe8BN7UzWuhg7kGmbJhCmiONZx6+nzv6PSQClTpQravwufTz58ycOZPZs2ezZcsWzpw5Q0pKCrt27SrpOPPFF18QEBDAmjVrzg8TLq8SKhVhYWE1qH7dOxewSFLd/PIpJFXJPgRBEK5lGdOnU7xyVallpo1/IdvtcHYocuHixRVuv/o6iW/6Vf272i1fWsAiyzI/HP2BN7a+QTP/ZiwYtoDmAc0vqUyhYjVuNnC5XCxatAiTyUT37t1JTk5GkiS0Wm3JOjqdDoVCwcaNGy8asBw9epSIiAh0Oh3du3fnjTfeIDo6usL1bTYbNput5HVRUVFND6NS5wOWumlhkSQVsuyqk7IFQRCuJNYDnpaPpn/8jrZJk1LvuYxGXPn5KH18kGXZk1Lc7UZ2u0GGfTdeT3xAPItHvo1TduJ0O3G4HeU+O91OfDQ+dAztWOO6WpwWZm6Zyc/JP3N7/O1M6joJjVJzSccvXFy1r8J79+6le/fuWK1WDAYDixcvplWrVoSEhODt7c3kyZN5/fXXkWWZF198EZfLRXp6eoXlJSYmMn/+fOLj40lPT+fVV1+ld+/e7Nu3Dx8fn3K3eeONN3j11VerW/UaORdM1F3AohQtLIIg1CvbySKKVp1EUkhIagWSSoEsg3fnRqhC9KgCdHW2b/upUxT89BOSUoWk0+Hdt0+ZYAVAaTCgNBgqLMeqldAb/C9LC0eWOYsJqydwovAEM3vNZGSzkXW+T6EGszXHx8eTlJTE1q1beeyxxxg3bhwHDhwgJCSERYsWsXTpUgwGA35+fhQUFNCpU6eL9l8ZMmQIt956K+3atWPQoEH8/vvvFBQUsHDhwgq3mTJlCoWFhSWP06dPV/cwqsxdyy0sDkch2dkrycpaTmbWH1isqaKFRRCEemU9nIc9pQiUCtx2N5YDuVh2Z5Pz+T5y5u+v033nf/c9uZ/+j4JFi3Dl5uKdeF2NylHb3Sj0XrVcu7IsTguPrHyEXGsuXw/9WgQrl1G1r8IajYbmzT0RbOfOndm2bRsffPABc+fOZeDAgSQnJ5OTk4NKpcLf35+wsDCaNm1a5fL9/f2Ji4vj2LFjFa6j1WpL3XqqS7Xdh+XUqf+RcvKTUssCA3vXStmCIAg14TY7UQXrCb631QXLHBT8dgLHmeI63bczKwtcnon/mi79BVVISI3K0TpAodfXcu3KmrJhCqnFqXw3/Dua+YsJCi+nS242cLvdpfqTAAQHBwOwZs0asrKyGDmy6hGo0WgkOTmZe+6pWh6Tunau9UOhqJ3shFpto/M/axrRMuF1/P261krZgiBcnQ5vzSAvzYRaq0ClUaLSKFFrlag1SlRnl6nPLlNpFCXvK1VVa0R3mx0ovEpfDhReapBlJG3t3Q6X3TLOHAtccOsp6PEnACj69VfcFkuNyrXbzKjcoPSq2xaW17a8xupTq3m1x6siWKkH1fpNnDJlCkOGDCE6Opri4mIWLFjAunXrWL58OQDz5s0jISGBkJAQNm/ezNNPP82zzz5LfHx8SRkDBgzgpptuYsKECQA8//zzjBgxgpiYGNLS0pg+fTpKpZI777yz3DpcbrLbAdReC0tExB1oNMGcPPUpRUVJ7N79AKGhQ2nbpuIUdIIgXNs2fH8EAIVSwmF347S7qjTLqkIheQKYkuBGifqCgCbQ6iQyQAOH8tG3DS6zvdvmQqGr/LtPlmVkmwtJqQCVVOFsw6btGRT8VLb13JHtGWSRPGgICv8Imq9aitK76v1mrEbPwAtlHbWwON1Ovjv0Hd8f/p6uYV25ucXNdbIf4eKqFbBkZWVx7733kp6ejp+fH+3atWP58uXceOONABw+fJgpU6aQl5dHbGwsU6dO5dlnny1VxrlbRuekpqZy5513kpubS0hICL169WLLli2E1LBZsLbVdqdbhUJFaOhgQkIGYrGc5tChlzAaj9RK2YIgXH0cNhc2s5Mb7mtFfKIn/YMsy7gcbhx2Fw6bC+fZIObcz55n19llnvWcdhdOm+vsz55lPmlGXDkKtMF6dC0CyuxbtrnKtLxcyJ5mxH6qiIIlyaXfUEpIKoWnFUV59lkl4TJ5brEHP9QW2ekGhxvZ6cZVHIkpQUfR0u9x5aZy+uEX8O7RnZAnxlbpM7KaCwFQaWunheVw3mGWJi8lpSiF1OJUzhjPYHVZGdtyLC92e7FW9iFUX7Wuwp99drEJ/eDNN9/kzTffvOg6KSkppV5/99131anCZVdXo4SKjQdIPvYW+QVbSi3PPnmCI1v/om2/gfiGhNbqPgVBuPKYCjy33L39z/fbkySp5NaQvuKBM5U69uIGTOEGYiZ0KPd9t9WJKrDilo6CpcexnygseR1wWxw4ZWSnu8zj3HJNtA+6Zv5lyvLp/RCOtDSMK77DsmMVlh2rCLr/FhT6yvsr2ixGAFRel5a5VpZl5uyewye7PyFYH0yroFYkhicS6RNJl0ZdSAhKuKTyhUvTcFLKNlCyXLu3hHJy1nLkyL+wWE+V+/6Xk54EQOvlTZfhN9XKPgVBuHIZzwYsBv/aHWggyzIqWcblXfFlwG12Yvo7w9NiolYgqZUlfU8klQJXrgVDzwj8Bje56K2gqvIbORjjivP/xFYlWAGwmT0dgzX6mgcssizzwc4P+GzfZ0zoMIH7296Pupb6Lgq1QwQslahuC0uuKY35u14nKXsPEzo9h81pRavyQqWALad+5VTOJhqrrCSGtMTpNKHRBNOs2XMl28d1H83xHSvpOHh4nRyPIAhXlvJaWGqDw+xEJUm4vCtOdubVPgTbsQLsKUXIDjeyw+VpMXF4HsigDvNGUlc7Q0a5fG9IxGvL3xzt3h1kFwdbJuCVmIirsBBlgD+G3n0IuP02FN6lAxOH2dPCovaqWXOTLMu8s/0dvjjwBS90eYF7W997ycci1D4RsFTC5bKT75Q4kbcLSVIjoUAhKUCSAAmFpEBCgSR5Hj8fmM385PUAPLju5XLLVEp6Hhy4iPWp65n05yS+b+LHntPraObXjBHPPAg8eNmOTxCEhs1UYEPrpUKtrd3pQSx5VgBUvhUHLH6DYmFQxWXIbhlJcWmtKv+k8vch7NX3yZjmaW125ubj1bUTzoxMst59l9xPPyVs2iv4DhlSso3dYkYDaHQ1a2FZfWo1Xxz4gindpjA2oWr9ZoTLTwQsldiYfYTX0vWQ/nCVt9FI8FDcQGYdXgGAn0pFB/9wHmj3OMvTdvPNoe94+fmuHGksQSOJ23+9vWTbveP21voxCIJw5dq8OLnylWrAejZg0fjVPJ18bQcrAM5cC7YzAfjd9Tl+gyLw7hZT8p7jzBky3/4vZ56diOx24zdsGAB2i9ETsFykhcXpdvLr8V9JykoipSiFQ3mH0Cq1tPBvQaY5k0ZejUSw0sCJgKUSTo1nuN071z2DjNszhwUysuxGRvYM5yu1XCbSvyWdIgfzaDkJG7fkebLyPrTcTZ4Bcr57k8XHFrMjcwcANpcNrfLyJMUTBOHaZS+wIQHaOky7Xx2yS8a46QxFK06i8NEQ8kh71KGlR/2oGzem8XvvctpiJvezz0oCFqfFBIDOy7dMuaeLT/PHiT/4+djPnCo+RcvAlkT7RPNg2wdxuBwczDtIY5/GPNhGtGw3dCJgqYRTduOt9mZg/AO1Ut7oZqPZnLaZtx44xo3X38fY5qMY2nQonb7qBCCCFUEQSglv7ofPRUbq1JS90IYW0AfVb8AiO92Y92RTvC4VZ7YZQ48IfAfGoqjgFpgkSQTcdhupT0zAevgIuvg4nBYzAFqv8/PPJRck89Guj1h9ajV6lZ5+Uf34b9//ipE+VzARsFTC4XbUak/xcEM4Xw75ksw+mfyd8TffHPyGFSmeW0cLhi6otf0IgnB1sBQ7CI0t23JwqZzFdrSAtpY781aV7JIp/vM0xr/ScBsd6FoGEnhrHJqo8ie9vZChd2+UAQEULFxI2Csv47J5MuTqvH05WXSSWbtmsSxlGRGGCP7V418Mih2El7ru5xkS6pYIWCpR2wHLOQ+vfJjjhccB0Cg0fDbwM9qGtK31/QiCcGUryrZwIikbnwAdeh81QY0NBDW+hOQrZ7mMDhwyKNS125m3KmSHi9xvD2M9lItXx0b49GmMulHVO8xKGg2B48eT/f77uI3FeJ06gBt4duMLbMnYSrA+mKmJU7mpxU1olDXvoyM0LCJgqYTDVTpgcbqcKBRnRwoBxZYibHYzwX5h1SpXISkYED2A5IJkxsSNoVt4t1qttyAIVz7ZLeN2yxTlWNm46GjJ8ifm9L/kst1mB87aGY1cbflLkrEdzSfo3tboWwbWqIyghzx9Top+/x3v4iKONNPilF1M7DyRW+NuRadqGH1zhNojApZKON1O1Eo1DpuVT154EGdmAQA2bwnJ5kLjVOCSZO6b/T9CAsIrLc/qtLLm1BpOFp3EW+3N0puW1vERCIJwpXI63WWWteodUTuFW5w4XTLZe7IJaB6A6iIp+Gub7XgB3teF1zhYAZAUCoIfeZjgRzwjOFsDItXm1U0ELJVwuB2oZCV7dvxZEqw4G3nh8lchG21I/j6o9+dgMhdVGrAcLzjO46sf54zxDAD7c/aLUUGCIFTIlO9JGjfq2Y5EtPBHUYvDiCUZfBQStgWHOOyvpfWLl6eVV3bLuI0OlBfJ/yII5REBSyWk7an0Xu9iHR8C0PqJuxnc546S95eunM+R/T/w/fMTS2937gcZlPL5LxldbytvDH2DuIA4ZFkWwYogCBUy5ntypRgCtLUarAC0eLoj+YfzsSw8jF+Bjf3TN+N3fWMi+0XX6n7+yZlnRXa4q9VnRRBABCyVipeiOeGfTfCQRAz+AfTvUbrRsW+P0WRnnsZpt5/NfgtIUknAYjuShi053bNYreTTO74hJLjxZTwCQRCuVMb8uplHCEBj0NCocyOOnSjEdDgfQ5GNot05UMcBiyPtbBr9CBGwCNUjApZK6JU6ggPDuXv0s+W+7+vtz/13v1Lh9kXZWXw64X4AHnz/U3yDxQzMgiBUjbnIDsC3//4btUZRMkPz+Z8VqM8uU51d5uWrIS4xrMotMs3HxAFwfPIGJH3NLgkuox1Hphm3yYGkVCBpFCj9tSi0SpwFNpTeapSBOiRJwplrQeGlQmkQt4SE6hEBS2VkmZpMQOp2ubBZzGi8vEjodT1pRw7iExRS+/UTBOGq1bxLKC6nG6fdhcPuxmlzlfxsNTlw5rtx2D3LnHZ3yUSJjZr4EhBWvRYMJTK64wUcfXEDMiBLErIEssLzjEJCVkigVBA8JJZGnRvhNjvI/zkZy55skC9evqqRF7pm/thTi1F4i1mQheoTAUsl3C4XCmXVPya3y8Ufs97l0F/rSy0PjIi85KnXBUG4tvgG6ek6rEmV1z+elM0fc/ai9ap+QOC6LgJbhgnZJYPTjexyg0s++3CDG3DL+BbbyNueSWiHEHLm78eRbcF/VHO0zfxQGjTILjey3Y0z34pscaIM1OEqsGHenY31SD5uixO/4U2rXT9BEAFLJdxuNwpl1RIrFefmsPOPXzi06U/63HUffqGNkGUwFxUQ3bpdHddUEIRrnbnIjiSBzlD9gKXpTc0rXcdpc5E+7S/UPmrsZ4zYTxUTfH8bdHEBZdZVXTidQIQBfaugatdJEC4kApbKyPIFQ34u7uf/ziTz+FFComPpOvKWuq2XIAjCP1iK7eh8NLU+ougcU44FSZJQ++sqnOtHEOpKPeU5vHLIsowkVe1j6nn73Xj5+ZN9KoWTe5LqtmKCIAj/YCmy4+VTd/1DLFmeSQZ1gTpUQXoktQLLvpw6258gXEgELJWQq9HptkmHzoyZ+m+0Ci9O/7UL45kczEWFOB2Ouq2kIAgCYC62o/epu9E31lxPXhjHr8c5M2MTssONJrLyyQoFoTaIgKUyssw/7wn98ssvdOjQAW9vbxoFBPPG01PJSEvnzttvp03Hrkz56TcenfEGU299jqZR0YQH+pPYuiXR0dHo9XoMBgMGgwF/f38AZsyYQUhICB06dKBDhw7cddddpfb3448/0rZtW9q0aUObNm1ISUkBIDs7mxEjRtCuXTsSEhIYN24cFovlMnwogiDUhMPuwulwIcuVDKmpIUuxA686yiAryzIH92STC+jbBeM3uAmNnu2Ed7fqzaMmCDUl+rBUxQVNLMuWLePxxx/n8zn/R/R6N06HghxTHodnrKJJZgC/3jGXYIM/S0+t4+nvX+OZYcOI9Iafdu4jPy+Pb7/9ltGjR5fZxV133cX7779fZvmuXbuYOnUqa9asISIiguLiYpRnOwHPnDmTFi1asHTpUlwuF8OGDWPevHk8/vjjdfVJCIJQQ4XZZr6ZvhXZLSMpJNRaZaWPPWtTiW0bROP4gH/kXzmbd0V9Pv+KSqPAmGsmKFyHxWKhbdu25OTkUFBQUKoemZmZJCQkEB0dTVJSEgBHjhxh8uTJbN68GavVSuvWrXnrrbfo2bNnyXZZKcUcP1JI0/taEZQoghTh8hMBy1mSJDF16lR+++03TCYT06dPL2npSDqazBudOuF0Ojlx4gTPPfccPTon8svX83l19SzimzXh4KlkNGo1/R8aRLsR3bE+/TsGjRdhnToQUJzDCB9/Nn/+Dctmv0/BhhXoDAY0ei/6jXvoovV65513mDhxIhERngnPfHzON79KkkRxcTFutxu73Y7ZbCYyMrLuPiRBEGosL82E7Jbpc0cckkLCYXXhsDlx2Fyeh92Fw+rCanJQnGfFYXMBkLI3lzNHCnDaXVSlYUae/RqPP7mCIKuNLKuFpI49kdVaZJUGWaVh8qGdNJM0FJ44zY4xj4FWx/6iXFoXOXhq1AP4+wfyy8GdDB5wIytnvEdAQAgnsrw4nqYhIMyLph1FPimhfoiA5QKSJLFr1y6OHz9Oly5d6NmzJ/nFRj5c9DObtm6ladOmGAwGZs2axTfffENWajpFVhNP3n8/v/xnNQsXLmTCtCkcHDuavXv3YnZYGTByNF26dMFkMjHls2/4fstOvt20nUC9jgEtmxLTtiMAixYtYu3atQQFBfHKK6/Qr18/AA4cOEBsbCx9+/alqKiI4cOHM2PGDJRKJa+88gq33HILYWFhWCwWxo4dy8iRI+vzIxQEoQLGfBsKpUSbPo2RajCKR5Zl3E4Zh92Fy3EuYZz7bNI4F7ZiK8s+O8SB4DD+Uml5bsQYnl/yBXL3G5FtVmSbjXUnDlEouxka0YRvUw5CdhqSw04bp512TjvS5pUoXA7GuO2867BxcNZMrvP2JlzrS9SHP9C8WzhqjRgdJNQT+SpQWFgoA3JhYWGNywDklJSUktejRo2Sv/jiC/nlB+6VW8VGy7Isy4d27ZEBWSFJ8nd3/1f+Ysx/ZL1GK/fv379kOz8/P/no0aNyVGCYHO0fVrLcZDLJCoVCLiwslNd8/bk8NrGDrFIq5G/nfCynp6fLdrtdlmVZ3rhxoxwSElJSl7Zt28pDhw6VzWazbDQa5f79+8sfffSRLMuyPGvWLPnxxx+XnU6nXFRUJF9//fXyp59+WuPPQBCEurPpp6PyFy/9VWfl5x9JlT94aIWcENNcXr9+vbx27VrZz8+v5P2CggK5adOm8pEjR+R58+bJ7du3r7CsPXv2yCqVSj6x/7Cc9dOv8oH4lnLxunV1Vnfh2lWd67fodHsRF2amLUg+Q9Hn+wFQK9U09YlFr9bh5+3L2rVrMZlMJds89thj+Gi8iAg8P29QSkoK4eHhYLey85cf6RTTmOHDhpN04jRhYWGo1Z6hiD179qRjx45s374dgOjoaG655Rb0ej3e3t7cfPPNbNmyBYBPPvmEu+66C6VSiY+PD2PGjGHt2rUVHo/b5aYw20La0Xy2/36Crb8cr7POf4IglFacZ8MQUHezs5vSc1m1+3vatmxNnz59yrw/adIkxo8fT4sWLS5aTkFBAXfccQcvvfQSsa3iCB49FF27duR98UVdVV0QqkQELBeYN28e4AkuNmzYQO/evYmPieJUZjZ/zvwJf50fAX7+BIUGE3dPNwAy8rORZZmMj3by5dv/h9PpRKFQsODW9zmScYpDhw4BMP35iXSMbsznTz8MQHz33mj1egBSU1NL6nD06FGSkpJo27YtAGPHjmXFihW43W6cTicrVqygffv2ADRt2pRly5YB4HA4WL58OW3atMFudeKwu8g+VczO5SfZuy6V/AwT38/cxtevbGbxO7vY+ssJtv+egtstAhZBuByM+VYMAbrKV6yhA7v3s/HAUma+Mr3Mexs2bOCvv/5i8uTJFy2jsLCQQYMG0atXL2bMmAF4/gnzGTAA06bN2JKT66LqglAlog/LBVwuFx07dsRkMvHhhx8SGxvLLknNC33HMPX3j3C4HfgF+KN2KTny9TaI9cLPxw+9Vsvwj+7nTG46sYERTO/6KEFOf16/+VlGjx6N0+lEaSykV1wsNpsNhULJ4+98TFZ2NuvWrWPq1Kns2LEDlUqFUqlk1qxZNGvWDIA77riDnTt30rp1a5RKJb179+aORx9n/J7jJE56mW+nTuantm2xOp306d6dAR3G8Okzf5Yck0qrxO1043Z5ApPEkU1o0j6Eg5vSObE7G6VSxKyCcDkY822EN/Ors/I3b/+bYks+1428ASQJh8NBcXExwcHBdO3alePHj5d03rfZbFgsFoKDg9m7dy/h4eElwUrr1q2ZM2dOqRZmXatWADjOnEF79rtJEC43Sb4K7gkUFRXh5+dHYWEhvr6+NSpDkiTy8/NLcqOc8+cTs2jq45kHKM8ri6ienZk8ZTI/Hl6JExeSJHHo0CEOHTpEv3790Ko0KCVPpzQ3Ll6ePs0z+mjBVzz61NNkFRahUqqIDG9Er6hGPDfzP7Tq3a/UPodsSKIw7Qyv+CoYMmRIyfKThXkk7jxV4TGEalS8n6nl4IrTdBvRhLBmfkQ098dudZJ3xnPLKqKFP5JC4rdP9uByuhn5VIcafV6CcK0ryDRjMTpQaz3Dis8NRVZplGVS48tumTlPrqPXrS1oe33djORb8+oCthx2cc9/rkeSJDZv3syDDz7I/v378fX1paioqGTdRYsW8b///Y/ly5cTHh6OyWRi0KBBtGjRgvnz56NQlP5Hxn76NMk3DiR63ud4d+9eJ/UXrk3VuX6LFpZKNG7eCjJBE+dHcIYG08rTvD3zLebeEM369et55plnCAsLIywsDFmWWbPkR5J/+IVAbTgZ7hzuvX48AMPG3sOhocP55d03MOXnUpiVCcCKuR+WClh+zMhjlxMIbcy21cvwUvjRzHsBkau+YkKHj8GvbYV1zbI7Wb8ll1Dg76UnAEgc2ZTOg2NoHF96crL8DBOxbYJr9bMShGuF7JZZ+Pq2kqHH/6RSK1Bplag1SlRaJZIEbpeMIbDubgnJVmikNxAVFQVASEgIkiSVpDq48GIQEBCAWq0ueW/x4sVs2bKFPXv28NNPP5WsN3fuXE8/ubPbugrPBz2CcLmJFpZK5P90FNPfGagjvNG2CEAXF4Cumf9Ft9m95FdWfTsHH3UQLdomEtS5KU07d8MQEFiyzoE/1/DHrHdLXjeOSWDwLc9iXJzM/5pqOGbOo/shTwe9of4zaaLbjl1Skd37JRr3fw6APcUmBm4/CsAvHZvz0P4U2v5dRI8UO7LNXVL2PTO74xukL3ntdLj4v6fW03dsPK17N77kz0gQrjWmQhvzJ/9F79vjaBTr68mhYnPhvCCfisN+/rUsg0qloMuwWDS62vk/ccWkBWTlghI3CgUY3V5oJDv3/O+OWin/QuZt2zh5z73ELlqIvm3F/zQJQnWJFpZa5D+iGb43RKP0rXrv/vajh+MVHsjOP37h8PHNmHctQ/31Z/S49S46Dh6B3WgmwC+coU+9wO8fvg1AXuppjIs9HdqGpTn4Wl0IeEYZbQ3pgivoACGtnqFxt+dK9tPOx5vP28Ry/74UjqSlE5ScTvcmkdx+exR+IXrmPrkeoFSwAlCYZUGWISDc+1I+GkG4ZpkKbACEN/MjJLp+5tI5leuNVrLi5eVp8VG7zMQk+F9Sma6iIlxFxagjwpHO3hYyb99O2ssvo46IKOnLIgj1QQQslZDUCpTq6g9FbJHYgxaJPQCwGo38tfAr1n/1OQf+XENWyvEy64fpm3jWVUDXGb3oIsusWrwZyaihY5uJBLR5HUU5CZsCZDePrl/CMeCWjD5AOt8tS79o3fIzPDOuBoaJgEUQasKY7wlYvP3rbpjyxbgcTuwqL9rFQ7eJo2qlzOI1a0id8CS43ajCwtC3a4cjNRXrgQNoWyUQ+eGHSEqRNE6oPyJguQx0BgMD7n+MVn36s/Z//1eyPEQbSWPvOKKj29Koaxz69iGoQ7wATyfgG2/uUWnZ4ebz95RlyYUkl/1COTd3yTn5GSZ0BjU6Q91NQy8IVzNjvg2FSkJfT39DRQeSkSUlWrniyU4rm0+oZVwcId5efDv5eXTeBqwrV6NUSDSb9Qk7Fi9h4KyP6d+kKYvmzMbQv39Ji4sg1BcRsFxG4c3jGfvmO9hzzUhukBQSykBdqeGD1RUdHQ2A2na+U+2I51sR3TwMq8mB2yWXSQOen2EmIMyr5HWO0YafXo1aDHEWhCox5lsx+GtrlGK/VvZ//AwAG4+Gsu2BxQSojQQ20pP4eH+8Gnn6yk2bNo2YmBhycnLKbD9hwgSivL3ItZhZt+l8skllQgwxCfFMeelvevbpg87PD58bbrg8ByUIlRABSz3QBHlVvlIVKRQKAgMCUR5sA0BIrDcp24vIOGRBb1Dj7a+lURNfvP3ON13nZ5gIjPLh+22nWLD1FLtTCwFIeXNYrdVLEK5mxnxbvd0OAggf0pNOR37DVGDjYI4Si1NDWpqE4cultLrrBvYeO8ayZct45513uO2220pt+/PPP5OXl8d10RGsOJLME//3Ndb8fIozM/jts0947LabaRIVRVyr1iWzOQtCQ1CtgGX27NnMnj2blJQUAFq3bs20adNKcoUkJyfz/PPPs3HjRmw2G4MHD+ajjz6iUaNGFy131qxZvP3222RkZNC+fXs++ugjunXrVrMjugY99fRTpBzM4MT2fPIzzKQdzcdidGA1OkoSxvkG64jrFkbr6xuTkWpkUXY+2w84S5XzzorDPDWghWhpEa45G45ms+pAJj46NT461QXPnp99dSp89Z5lerUSU4GtTrPWVkahVtPpyREc33WQ3R++VLL8z+2w9u/v+XDVX4zs2IrfZ07DYTbz00P3kpGfg9Pl5rWVG5jYrydJxU4csozOzx+dnz/+sU3oqVLxyoAbeLZRECcOHRBTdwgNSrUClsjISN58801atGiBLMt88cUXjBo1il27dhEbG8vAgQNp3749a9asAeCVV15hxIgRbNmypUwionO+//57Jk6cyJw5c0hMTOT9999n0KBBHD58mNDQ0HK3EcqKTQgjNiGs1DJZljEV2Mk8UUjqoXySVp9m++8pqICoGF8eHdSUAQmhZBRZGfz+Bj5ac4yP1hxjdIcInhrQgqYhhvo5GEG4zD7dcIJdJ/Px1aspsjow2pxUdK1WKiS0bnjYL5yBl7GOsiyza8Up9qxNxVRgQ5bd2ApnA9Dn7kkoi/PQOfOZs3gJnePj6d4ojCM5eYBMYVEBKiQW7TnIgLjmtAgMJCkzE7VGU2ofL0x9hbfefY8mKjfr33qbHLuTouwsfEPEd7FQ/y45D0tgYCBvv/02UVFRDBkyhPz8/JKx1IWFhQQEBLBixQpuqOA+aGJiIl27duXjjz8GwO12ExUVxZNPPsmLL75YpTrUZR6Wq4ml2M7aRUc49ncmO9p5MfehbujUnk66siwz7ef9fLXlJACvDG/FA72a1Gd1BeGyGfLBBrrEBPDv0Z5bq263jMnupMjqpNjqoPiC5yKrk+mL99FMq6FLhD96rRIvjQpvnQpvrRIvnRqDlwqDXo2Pl9rz7K3Gx6BBp1HWuM/a8aRs/pizl1Y9wwlr5o9Ko+Cv794j9/Q+Rk+aRrPO3Th27BgDBgxg165dBAYGsm7dOkaPHk1BQQEbNmzgscceY+fOnWg0GubPn8/7779fctvn66+/Zv78+axatQqA556cwOrflvLMTcMY9/bHotOtUCcuSx4Wl8vFokWLMJlMdO/eneTkZCRJQqs9f19Xp9OhUCjYuHFjuQGL3W5nx44dTJkypWSZQqHghhtuYPPmzRXu22azYbPZSl5fmHJaqJjeR8PQ+9uwsXcjZn2xndd+O8Broz1JoCRJ4t+j2zC+ZywD3lnPH3vTyw1Y3G6ZV37eR9MQA/f3jL2kDsOC0FAcTC+iyOLg32dfKxTS2dtCakBfZv3vlh3jjNXGLyeycSBjB6jCn4IkgwZQI6GRPA+tQoFGKaFTKtAqFehUSny0Kl65qx0RoedbOXevPk14cz/63ZNQsqx5p9f56T+vsv7rz2naqSsbN24kMzOTuLg4gGrNJ7Rq1Sq2bt1KcLAnA7bZbMbpdPDUJ59z57/fRusl0iAI9avaAcvevXvp3r07VqsVg8HA4sWLadWqFSEhIXh7ezN58mRef/11ZFnmxRdfxOVykZ5efl6QnJwcXC5XmT4ujRo1KpnluDxvvPEGr776anWrLpzVq0UILw5pyatLD/Bgr6bEBp//Itp2Ig+A7SfzkWUZSZLYeSoff72apiEGVhzI4JutnvmMYoO8GJBw8f5JgtDQ2Z2erNA9mgVVeZvfZgwo9drlcmM2Oyg2OSg2ex5GswOTxYHR4sRkc2K2OjHbnJjtLsx2FxaHC4vdhdXlxup0YXW6KbQ7STPbSXM76bU9nbuGtsBqcrBrxSnSjhYw+OE2pfYrKRR0HDycxW++yoKpE7EWFzN9zDD8Q8No3e8GMq1OHnzwQZKSki46n1BoaCjvvfcer732Wsn7r82Ywfo/fuXxm0eIYEVoEKodsMTHx5OUlERhYSE//PAD48aNY/369bRq1YpFixbx2GOP8eGHH6JQKLjzzjvp1KlThf1XamrKlClMnDix5HVRUVHJ/BlC1YzpHMm/fz3A1hO5pQKWvvEhJT/f+N6fjO8Ry8tL9gHgpVFitp+fO+VollEELMIVL7PICsDIDhE1LkOpVODjo8XH59JHDm1OyuDO73agc8qsW3CYw1szwC3TZVgsTTuGlFk/tn0nOg8bRXFuLhFxCWi8vDlzaD/bvv6MgOv6Vnk+oYCAAAICzqdHsORkotVoePg/H1zyMQlCbah2wKLRaGjevDkAnTt3Ztu2bXzwwQfMnTuXgQMHkpycTE5ODiqVCn9/f8LCwmjatGm5ZQUHB6NUKsnMzCy1PDMzk7CwsHK3AdBqtaVuPQnV56PztJjsTyt9Oy3cT8+KZ/sw8L0/OZZl5OUl+xjUuhFdYwNZcSCTHKONNhF+HMsycirPXE+1F4Tak17oCVjC/cre+qkP6Tmev6vDy04R4aunw4Ao2vWLRO+jKXd9hULJ9fc+VGqZLMus+t8s9q1dye4N68rdbvz48YwfP77c92RZZkB8E27udR1ar9pLwyAIl+KS87C43e5S/UmAknuga9asISsri5EjR5a7rUajoXPnzqxevZrRo0eXlLd69WomTJhwqVUTKtE1NpDf9qQzaXBLDNrzvwpNgr1RSOCW4Z7rYvjXqNZIksSDvc8Hno98tZ3T9RCwnLTYsLpL9xOX8by+sPu4n0pJhK78L3hBuFB6oSdbbLhf/Q1TvtCJ9GIA2rQNYdjDbVHWIM2AJEkMuP8xCrMyWTbrXca/O7tat3VMBflkpxyn64ibq71vQagr1QpYpkyZwpAhQ4iOjqa4uJgFCxawbt06li9fDsC8efNISEggJCSEzZs38/TTT/Pss88SHx9fUsaAAQO46aabSgKSiRMnMm7cOLp06UK3bt14//33MZlM3HfffbV4mEJ5JvRvzo87U5m2ZB//vbU9irNZO9VKBVtfuoFtKXkMbh1WbsfayAAv1h7Ouqz1XZ1bxF17ys7DVJFDvdrgrxa5Ea9lx7KKuXXOZgK8NTT212PQqvDWqjBoPTlWvLUqkk4V4Hv25/pmdbhYk1lAkEvihrsSahSsnKNQKhn4yFPMn/gYa+d/yuDHn6lw3YKMdPLTzxAUFYNvcAg6bwN6Xz92/bGUmHYd8fL1q3E9BKG2VOsvNCsri3vvvZf09HT8/Pxo164dy5cv58YbbwTg8OHDTJkyhby8PGJjY5k6dSrPPvtsqTLO3TI65/bbbyc7O5tp06aRkZFBhw4dWLZsWaXJ5oRL19hfz9tj2vHM90mY7S7+e1v7kpaWEB8tQ9uGV7htI18t2UW2Ct+vbbIs8+/kNBK8dbwdX7q/ksz5ARoS8ENmPvPO5KARwzCveRqlknyzg3yzg4QwX4ptTjKLrJhsLow2z1Blk91VrQ63den//jzOsQIz3z7ZHS/fS28h9A0OYcADj7Hsk/eIat2W1n0HlFlny4/f8dfCrz0vJInOw0bTZ+x4bpo8jSVv/ZtvX36eUS+8THBUzCXXRxAuxSXnYWkIRB6WS7NifwYTF+6mVYQv8+/ripem8jj25w07ObrsE56Y+iH6y3CP2+xy0/TPPdwQ5MvX7crvE3XO7FNZvJOSwbE+7eq8XkLDlpxtZMRHG+ndIpi593Qpd51zX4H1PUTf6nBx43vr6d40iLfGtK/Vsn96cwa5qad56OPPSi3f+cdS1s6fS9dRY+hw41AObfqTjd99SaMmzUi86Xa8AwJYMPU5AhtHcd+7s2u1ToIA1bt+i39BBQa2DuOL+7uy70whYz/ditXhqnBdl9PBri8nM2p1P55XL0K7YDSc2VHndTS5PHW6PtCn0nWz7A5CNWIm6mvdr3vSGPDOepQKiYf7NKtwPUmS6j1YcbrcPPNdEllFNsb1iK3Vso9t20LqgX34hpQdYbR//SriEnvSZ+x4fENC6TZqDLdPfxOAn//7GgumPgdAXGLlM8cLQl2r/5u2QoPQOSaQbx+6jtvmbua5Rbv56I6OJX1aLnRw3fd0PD6n5LXCVgSf9oeEkdD/FQiJu+S6TDx0igXpecR76/BRKjAolazL93REHBZS+b30bLuTkCq0EglXt4XbU+kQ5c93D19XktG5IbI73Uz+cQ8rD2Yy9+7OtI6ovf4iyz55j/3rV9Osy3UMe/L5svs2mzEElr4d1rhlK8bOfJeCzHSyT57A2z+QiLiWtVYnQagp0cIilGgf5c8Hd3Tg973pvLX8cJn3LWYzbTY+UfI659G98NhfMHo2pO2CTxLh5wlQmHpJ9Yg+O7rnsMlKcy8d3ioFrbx1RGjVhGsrv68vAhZBlmX2phbQNy6kQQcrbrdM3Mt/sHjXGd4e044bWtVe373jO7exf/1qAEY+NwW1rvQoqEOb/qQgM52oNmVvP0mSREBYBHGJPWkcn1DvLVCCAKKFRfiHwW3CeX5gPG8vP8w93WNo7O/JTbH/r9+Q187kXJ7NY6OW0jws2vOiw1hocwts/xz+/C/s/g78o8E7BLyDwSvI8xw3BKK6VlqHZ2LD2FxgYn1+MTcE+TI81P+i61tdbrLsDrLtTrLsDpItVgYGiVEN17Iii5MCi4PFu85gdbgY0jacDlH+9V2tMo7nmAB4uE9Tbu4UWatlG/NzAbjuljvhgp6Kuamn2fHbYvatXUXLnn1p1rlbre5XEOqKCFiEMm7rEsXbyw+z+3SBJ2CRZVqvHAtAnuzDoe5v0aNjn9IbqbRw3WPQ8W5PwJKfAuZcMOVAxh5PC0z2YbjjmyrVodHZ0UovHkktN2AxOV08degUSUVmztgcpd5TStDRVyS7upb5ealZ/HhPvtp8knmbUpj753Fmje3EsHYVj3yrDzanp29W55iAStasvjb9bsSUn8/mH7/l8KY/aZHYg+yTJzixazve/gH0ufs+Og0ZKVpPhCuGCFiEMpRn+66ce85JO07w2fccD62nR2TFHRjR+kC3h8ou/6izp9WlihZm5AMQoSu/8+wpq53fsgu5LSyAHv4GwrRqQjVqQjQqgtQqFOJL+JrXIcqfDlH+zLypDa2mLWPmbwcY3Cas5Pe6IWgV7kurcF9+35vOoNYVZ/euCYVCSfcxd9KkQ2d2LlvKgfWr8fILYPDjzxLfow8qteiYLlxZRMAilGG0OgFKcrKYP/NkKt4TMJC2EWVncK6Uy+lpcQm8+HDkc05azud3+Sih/NwPXmcTag0O9mNoiH/16yRcdSx2F59t9CQW9NKo8NIo8dKq8NYoeaBXEz7dcIKRH29k0aPdqzR0/3KQJAmDVoXTVXfZJcKaxzF0wnN1Vr4gXC4N469WaFDONVNrVJ6g4EzMaKJPfEy7/BVs++R+uk6YX70CC06C2wlBF2mZuUCKxQ5AlE5DvHf56dKDzmawXZFTJAKWOmQwGEq9ttlsJCQksGfPHgCefPJJlixZQmFhIT4+Ptx666289dZbaDSXf1qEv47l8N8VRwj01mA5OxvyP+1PK2Lr8Tz6tQy97PUrj9XhYndqAS8Miq98ZUG4xomARSgj5OyMs1lnM9kGtOrHluJ0rsv5ka45i7Ha/4euOv+h5p1Npx9YfsCyJreIBem5tPDSsaXAyOZCT0fEbn4Vz32yN9cITjc5DmfV6yFUiXFLOtYj+UhKiZOfbkdSSqCQkJQSvZ4dwagOAyn44wSSQuLeDqOZdtdEghNjyc3NLQlYXn755cte79R8Mxqlgu1Tb0ChkHC7ZSwOF2a7C7PdicnmwuFy07Zxw+iQbXW4mLgwCadbFrOeC0IViIBFKMNPr6atPpemPwyEn05xYQaGE6pmNKlmc/rx7DMsjx6Lo1BDcsYpuvl50zfQh8ZaNUaXm0f2p1DscgOFRGrVTG4Sxn2NgyucB2hfdjF3vrcRHdDoOhVnoi0lo5mEmjvXmiI73eAGu8tBi/AmrJu6CNkts/PEXg6dOsrCFYt557tZ+Gi9eabHeO7pOApn00bIkoxCoeDo0aP1Uv/UfAuNA/Ql+YMUCglv7bk5ghrO7O5ZxVaW78/kq80pnMw1M2tsJ5oEV31iQkG4VomARShDkiSmty8kIelUybIt4XcR1PshWrTqWO3y5pi8+bLJIxhOZaOSJBZm5CEDBqUCo8sNwNSm4cw8ns5NjQJ4Nrb8zoe5Vjsfbz/FzzvPABAdZmBlUjpL/k5laNtwHurdhHaR/tWun+BhNBoBcGSbyZ6zmxvmjOOuR8cR+pgnT8dHQ19Co9Uw97vPuK5lZ469uYEcUx6ztnzNR02HYDKZCAoK4j//+U+91D8130JkQMMNXNcdzuLlJftIzbegVEj0bB7MB3d0JCFcTCciCFUhAhahXF1GPg6q457cKqGtuC5MAblr4cApCG0NgU1AUbWEXKeL8ujHVhbc/DCSJJFtd7CpwMjeYgt7iy3c1ziYNJun34qPsmyZ+TYHr/+VzA/rU5Btnn4JbdqF8uvYrphsThZtP81nf51g5MdpdGsSyEO9mzKgZWi5mXqFyqlDvDgWb+RI+nHGDhwDgMlkYvny5TzyyCNcf/312E4U4q/zIebmdiQ2Gsob8XM5ePAg33zzDWFhtTvaparOFFho07jhXfzTCy1M+3k/Kw9k0qNZEC8OaUmPZsEEel/+fj6CcCUTAYtQPoUShr93NiHcPMjYCwd+AVuh532VHkITIKwNNOkLzW8AvX+5RZ3QN2ZI0a6SfA8hGjWjQgMYFRpAqtXOCbONbLunL8r7JzN5Kvb8/fzNZ/IZ/9UObAU2NP4a3rmnE5E+OloHe25feGtVjO/ZhHu6x7JifwafbjjOQ19up2mwN/f3asKYzpENOtNpfStYvATzli1oW7YEWcZ29Ci2w4eZe6yAfk0TCQ/ynIuvv/4at9tNUFAQcXFxFOUV0CW4NbN7jCWqWWcgkISEBNq3b8/48eNZtWrVZT+W1Hwzg1o3rL4gDpebcZ//TYHZwUd3dmR4u3CR90QQakgELMLFxfbyPABk2ZMILms/ZOyDzP2QugN2fnl+/YEzPcFLSDxIEiabhRR9JP7+StyyzGGTlfV5xewsNrO90ETaP5K+md1uHt6fwiORIdzyxjoAJK2Sd+7rzC3xFf/nrlRIDGkbzpC24ew4mc9nG48z7ed9vL/qKI/2bcrYxOgGM5S1tsmyDE4nUjXzamR/+BE5n3yCJiaGoj/+AFlGGRCAIyyMpYe38t7wl7CfTEHXrAP/+9//APjll1/4delSfHYf4olXZzHu6SdZc583vJwFCiUOh6Ne+rAYbU7yzQ5+TkrDV6/GV6fGV6/CV6fG54KfvTTKyxow7DiZz5FMIz8+1p3OMYGXbb+CcDWS5HNzq1/BqjM9tVAHClPh70/hr/fPL9P6gVcAq/Tx3N3iJeB8nxWdQqK9jxedfb3p5udNS4MOb6WCLLuDvwvN/Ds5DbPLjW65p6/KkwNa0CnanxahBiL89FW+1XMy18Qna5P5YWcqSkni/l5NyDfZkZHx0qi4pVMkbSMbxoiRmij48UdyP/scZ0YGqFR4demC22zGvGULKJVIWi3ePboT8vjjaJs3B8CclIQuLg6Fnx+HEloB0PLggVIX8fnz5zPpiWf5+4mfULpspLRT0mf8QAA+/vgjbjwTh5dbz8GsYwyadz+nnzUQ4SOxL1vi9h9M9IrV8X9jQkGhxqXSUYiaAN8QJJ8w8I2A66eA1lD2gC6B1eHitrmbOZFtwup04aggr4lSIaFWSjw/MJ4He1ctL9ClOJBWxNAPNzB5cEse7N0EtVJM3yYIF6rO9VsELELtspvg9N+eVPzWQsw2M5P0vXA3akuCjzcdfLzo6ueN7iJf3FaXm5nH01i/Iw11hoW0XEtJTo1gg5YhbcJ4qHdTooOqln7/ZK6Jez77m9P5ZqIDvQjw0jA81UYPjZagQD2SToVCp0ShUyGdfS75WasEpQJJKWHZl4PlYB7qRl6ogvWogvWog/UoA3SgkDwtUDIgy0g6FUpfDbZjBaCQ0MUFINVSnxpXYSFpU17CuGYNAIHjxlG8ahWOM54ATxkSjO+gwdhPnsS0YcNFy9LExNB02R+lApZevXrRURPK5G7PAjBz7Wx2pR8gtTCDB3uM5cH2N7G9TTLGtD3cM/k9AnRgl5WE+mi45YaujLutD39YZHROC98F9SXZy5Ph+Ju9k2hXfISQcT9AZJdqHbM1uYCcz/chaZQovVRIXmoUehUKL9XZZzXqMC+82oYgyzJWh5tiq4Miq4NCi5Miq4Niq5Mii4OXl+zj3u4x/GtUm8p3fIlkWealxXv59u/TNA3xZs7dnYlr5FPn+xWEK4UIWISritstc6bAwtGsYjYdy+WX3WmY7S6+fjCxWhPaOVzukv9wD7z4J7YALbHxQbitTmSr6+yzE/e5n22uUpPGnePVIQRHjgVntqWkE3BlNNE+hDzcDklV8/+wXUYjbqORY9f3A8Bn4EDCXp2BKsAzD41l334ktQpd/PkkZI7MLMx/b8W6/wD2Eyfw7tkDSafDuncf2hYtCLhrLNIFHZ0PHz5MQkICa++dQ7OwBADsCgk5MYw33nqT9UfW8eh9d+Ecruezf39OSEoKf911fmROqjaULtctAsDPWYxVocGmKD2keF8bX4JDKm/dyP/pKI4sMwqdClehDUe6Cd/BsbgtTmSzE7fZgdvixG124iywgctNxL96VHrLp92M5Yzv2YQn+ze/bC0eB9KKePq7XaiUCn5/qpfoxyIIZ4mARbiqGW1O7vp0CwA/T+hV7e2ddhcZ0zZxpHMQ/W9tVeF6sltGdrjB5UZ2ychu2fPf/dlOvLIs4zY5cOWfnUpAAiQJl9FO7rz9AHh1CkUdYaDw1+P43hCN7w3lTzVQFUd69caVkwOAtmVLmi5ZXOOyKjJp0iS2bt3KN91fK1kW9moPVFolHz28kiVb/48Nyb/gllzEdIzh6an3c3NIOJGtxkDecYbvPsl2XQy3uU/x4YCRJWVY7DbePHSUublOknu3xVt18Y7QskvmzMsb0UT5oDBokK1O1FE++A8pf2qIgqXJWI/kE/bcxVtuXG6Z+Jf/wOn2fO19eX83+sSFVPXjuSQ/J53h6e+SmH9fV66PbxiZdgWhvlXn+n119kIUrmoGrYr7ejbhme+TyCqyEupbfvr+iuRmePKN6AMuvp2kkJC0SqD8i6skSSgNGpSG0sNTnTkWANRRPgTcGockSRSvPUXRqlPYzxhx5lhQ6FWoIwxYj+SjjfbBq2sYumb+F62PKz+fgLFjMfTrh1dit6odbDXIDgeTmjajKNNcsizJ7MT7h6OkHspHoVByc/fHGDtrJGO6jsRP+4/+PxEdeEAZw/YDJ5nee2ipt/QaLYG+IQQUZlUarAC4im0gg+8NMejiKp/J2JlrRRVY+e+BUiHx+9O9STpVwKQf92CyXb5MyTanJ+fQ+HnbSHlz2GXbryBcLUTAIlyR+sSFoFEpeP6HPUwbnkDz0Kr3C8jLNuMD+AbXTZIxVbCexm+UbvYPuqcVRStP4jY5UDc24Cq0Yz2Uh6RWYDtdjDkpG68ujVD6aJBdMs5sM45MMwqNElUjLwLHtABJgTauBYbe1W9VqoqMf/2LgiU/43P9DRTpiilo0QSN1U3miSKKsi0kjmxChxujUV1kmLjZ5UYBqMvpr3PGZidSV7XcI64CT6uV0r9qGWqdeVa0zarWgTqukQ+usy0sjfyqF+xeipHtI5j0w57Ltj9BuNqIgEW4IgV6a5hzdyemLt7HDe/+SWSAnlbhvlzXNIiRHSIINlR8oSvKt+ADBARXrdNuedxWJ85sC858K5JSQhWkRxXqVdKx9p99FLSxfoQ81K7csmS3TOY72zFvzyy1XOGjwZFnxZFh4szubHyGz8L092YC7qhxtStUtHwFBYt+IOxfrxJw2201LmdTgZG2Pnp8/9GKIssyx4tN+JqLyTl9kuCoi98aKwlY/CoPWGRZxlVgRRVQ9RwsGUVWABpVs3XuUujUSl6/qS0vLd5LjtF20d9RQRDKEgGLcMXq37IR614IZuWBTPakFrI3tZA3/zjEh2uOsvjxnhXOz2Iu9FwMg6oxf4uzwIb9ZCG2E0XYThTivOC2yTnKIB2G7hEYrguvVudaSSER9kJXXCYHkkpC0iiR7W4UWiVuuwvbsQIKfj2OK8+K21G7SfBcRhMFPywi65138RkyGP9bb72k8rYUGBkZ6s/RrGzWHDnGjvwiZIWCTWjJ9fIlcecmlvy4l3H/nYVaU/EF23Q2eJM0lX+ObrMT2e72jNaqosxCK5IEoT6XN2gw2hxolArUCjG8WRCqSwQswhVNq1IyvF0Ew9tFAJBjtDH8w418uuE4r9/UttxtbEU2zMhodeX/+suyjO14IZbd2bhNDuypRlxngxxVsB5tEz98+kSiDvNGFaBFdss40k2Yd2ZR+PtxTNsyCLo7AXVI9VpwlN7nE795+s6AQqNE3yoIfasgTk9ahy4+rlpllsdts2Hdu5eiZcspXLwYt9WK/5gxhL089ZJGr8iyzBmbg9mns5kNgB4MerysZrqYchnsDf2H9GfJzLX8vXghPW+/B0e2Gdnm8gR4Z2eERqXwDAenbEtVeVz5ntYSVUDVg4+MIiuyDL8kpeGtVeKlUZ1/1qjw0irx1qjQqRW1NqJHlmW+2nKSYe3C8fOqXpI/QRBEwCJcZYINWoa0DWP5vgxkWS73YuM0OjBV0FDhtrnI/eYgtiP5qIL1KLzV6NsGo431RRPji9Kn/D4YyhYadC0CMPRuTN63h8j8YBe65v5oon3QRPqg8FKhDNCh8FLV25BW+8mTpM+YgWXHTmS7HWVgIAH33E3A7bejDg+/5PIlSWKoMYv9+UVEmguZNnQgsizTJrINKuX5r5quo8bw95IfiOvYE9u8M+UOHQfwH9WsSvt1nh2lVZ0WlpCzLSvPLdp90fUkCU8Ao1ES6qvlmweuq3GwIcuQXmClZaLIwyIINSECFuGqM6BlI+b9lcLeM4Xlzt4sWZxYVOUHDcVrT2E/UUjQva3QJQRWO7jQRBgIndAB4+Z0bMkFFK9LRbZfkKtFeXZkUaAWVYAOdSMvtM380UTW7UXMevAgpx54EKWPD6HPP49Xl85o4+NL5WCpDZ8O6cdPb75K3pnTtLr/7nJv+3QbfSuHNq5nzRdz6eEeRtCdLVEG6sAtIztlcMsgy2grGTV1jqvAiqRRoPCq+tfZXYkx3JUYg93pxmJ3YbI7MdmcmOwuzOee7U5MNs/z/rQiFu86Q7HNUeOARaGQuD4+hGX7M3ikb9WCMUEQzhMBi3DVSWwaSGSAngkLdjG0bTjxYQZCDDqCfTTEBHqjtruxqcvvQ2A5mIe+fQj6VkE13r9Cq8L3+ii4PgrZ6cZltOM2OnDmW3EXO3AV2XHmW3FkW7Dsy0H+IwV1pAFDjwi82od6bouU5xJaZrL++w5KPz9iFnxTkmiuLihVavqPf5j5zz3OyT1JNO+SWGYdtUbLgPsf5cc3pnMypAnh0d2qNCS5Iq58G0p/XY1arjQqBRqVotIgZPGuVBbvOkOQ96X1eekbF8KMpQdIyTERW40+VIIgiIBFuAqplQrmje/Ke6uOsHhXKplFtpL39Golbzu0+Bm0yC430gWZTl3FdpyZZnz6RNZaXSSVApW/Dvx15baiyC4Z65E8jJvTyV94hKLVp/C7MQavDrWXWMx+6hSmLVsImzatToOVc4IiPan4V8z9kOZdvil3ndgOnWnWuhtJB9cQnzqQkMCaz+vjzLdWq/9KTeQa7XhrlOg1l9YidXOnSOasP84Hq4/y3u0daqdygnCNEAGLcFVq0ciHT+7qDIDZ7iSn2E620cofezNI3ppJB6OLtNe2ok8IRN86CEmloHjjGSS9Cn3C5ZtVV1JK6BOC0CcEYT9jpGjVSfK+O4zb5sKQWF6/Ehnzjh3YT59Gl9AKbVyLi7YsOPPySJ3wJOqwMPxGDK+7AymHznDx21w3PPEEXz39JBu/nMdN//13jffjyrehia3bDNfZRhtBtTAM2Vur4qaOjflm60lcbhllLc0vJQjXAhGwCFc9L42K6CAV0UFedI4JRB6WgCPNhGV/DpZ9OZh3ZgGg8NUQOCYORT2N4NA0NhA8rjW53x7CuPFMqYDl3AwaRUt/Jffj8xMaqhs3xjCgP96JicgOJ87MDFxGI0gS7qJiCn76CUmSiPnmaxReNc87U10h0bGEx7W86DqGoCA6dB/C5j8XkrnnCI3a1WwElLPAit6/btPr5xrtBBuqlvSuMtfHh/Dx2mPsPVNYrbmwBOFaJwIW4ZojSRKaxgY0jQ34DYzFmetJpa8MrFk/iNqma+6PZXc2zgIbKn8tsixj2pqOJCnQJTQl4o1H0bVsiXnHTorXrKb4j2Xkf/kVAJJOh8LHAG4ZSaHAb+RIgh9/DFXg5Ws1AgiMjCYr5fhF1/n444+Z9/k89uxO4put+9mWfBTFPzoBZ2ZmkpCQQHR0NElJSQAcOXKEyZMns3nzZqxWKy0MUbwRNZMbro+qq8Mht5ZaWADsZ1P0ay7TxIuCcLUQAYtwzVMF1U2K/prStQpCueokWbN24d25Ec5cK5a9OXgnhuE/clJJvxtD714YevdCfuUVXLm5SGo1Cj+/BhF0NeuSyO8fvk32qRRComPLXSciIoJXpr3Cok/nsztpC7u+XEzn+8aUWmfChAl07NiR3NzckmUFBQUMGTKE//u//8NPbeD9O6Zz85N3sUu3luDQEBRaJZJGWfIsaRUotCrMshXZR4VGo0Gn06GsxgipHKOdNo3L3nb680gWP+48Q3axjc4xAQQbtOSZ7Mxen8zYbtHc2S2aZiHeqJQKZFlmf1oR/1l+mEa+WlqGieHNglAdImARhAZG6a0m9PEOFK446cn4qpAIvCO+wo64kkKBKuTyzDhcVXGJPdgUFs7y2R9w85QZePmWnucnL+U0EblWTuzeTs7JgwDknDpVap2ff/6ZvLw87rnnHt5///2S5d26daNbN8/kj7LTzf1jx/PGhrns2rCNntGdcNtdyDZXSX6XDKmAZGUGB1VnSsoIDg5mwoQJVT6ejEIrOrWCT9YdQ6tUoFUr0aoUvHDB3ECbkj1BVYiPFrvTzfxNKczflIJSIeGjU1FsdeJyy0QHejHn7s4oRP8VQagWEbAIQgOk9NMSeOulZ7WtL0qVmuFPT+bH16cx/7nHuf6eB9BrfTm27i9OHk6i0JSNAgWh/k1oHJmAItTEoOkTS7YvLCxk4sSJLFu2jL/++qvC/UgqBekdXRjtZnrNuImwsDAA3C43WYfPsGfTLjal7kQpK0gMa0fcje3Zvn07aWlpVT4WWZbJMdrINtrYlpJf5n2F5Ekdo1ZK/PZUb+IaeVpOCs0ODqQXcSyrmGKbEx+dmsgAPb2bB6MSt4MEodpEwCIIQp1o1LQ54/47izWfz+GPWe8CoFMaaBwUR2L/MTS/sSf6Rv4cnTGDvLP9U86ZNGkS48ePp0WLFhcNWAoKCrjjjjt46aWXSoIVgHn/nctpi2c+onZBcYx+7A4UZ+d32rdvHwaDocrHUWR1IgMvDm5J51h/rA732YcTWYanvvPU/cC/BqO+IBDx81LTvVkQ3ZvVPKePIAjniYBFEIQ64+0fwIiJU0hLOkDBz8fwMfsjSRKKkxoK5h+hQJIw/nUGa3oBGe9sR6FXMXrTKrYvX0b4+A9o8eN+ZJe73LILCwsZNGgQvXr1YsaMGSXL3W43mZZcQiQ/Rt1+M5EtS88MbTKZ8PauetK27GJPHp9OMQF0jS3befnjtcfoEhtYKlgRBKH2iYBFEIQ6F9GhFREdWuEy2rEdLcCZa/Gk4UdGtdsLpVGDrmUgRWeKOZCyE2dBJqmzxnHXLJBdDnDaCQ4OZu/evYSHh5Ofl8/gwYNo1bo178x8C4fFjkqnRqFQkH0qAztOYsKjygQrAEajsVRrTGXOBSzlDWuWZZl8s4NAr9oZ8iwIQsWqFbDMnj2b2bNnk5KSAkDr1q2ZNm0aQ4YMASAjI4MXXniBlStXUlxcTHx8PFOnTuWWW26psMwZM2bw6quvlloWHx/PoUOHqnkogiA0dEqDBq+Ons7DTqcTp9OJaqMPymIdugEReCkUrOjzH95eeiebT+QBYD68EePuFYx75iNWrs9i9FBvEjt1Re/nTXR0NP/9+D1P4TKoUKCSPKN/tqftY4jzJpSq0qOBjEZjtW4J5Rg9Acu5CRMvlF5oJbvYRrtIvzLvCYJQu6oVsERGRvLmm2/SokULZFnmiy++YNSoUezatYvWrVtz7733UlBQwC+//EJwcDALFizgtttuY/v27XTs2LHCclu3bs2qVavOV0olGn4E4Wr32muvlfpnRa/X07dvX9atW8eillHsOJnHLbM3o9AZQKnkR6OSH5NSWbLwdY6eTAY8/VE0ag2SJPHCQ8/QK7EPDrud46dTeOGtqSxZsqQkf4vNZmPQoEFs374dWZaJiopi4sSJPPzwwxetZ3axDZ1agUFb9nvphx2paJQKOsfU/ZQHgnCtq1ZkMGLEiFKvZ86cyezZs9myZQutW7dm06ZNzJ49u2TI4csvv8x7773Hjh07LhqwqFSqajXRCoJw5ZsxY0apvif/1DkmkNdvastLgKHtDSXL+3RKJECjIyMjE7W/jiVLlpTZ9tZbb6VrYrdS+VtUKhVvvfUWv/76K3fccQcA/fr1IyEhgd69e1dYj2yjjWCDttz8Nr/vTWdUh4haSyonCELFatyU4XK5WLRoESaTie7duwPQo0cPvv/+e4YNG4a/vz8LFy7EarVy/fXXX7Sso0ePEhERgU6no3v37rzxxhtER0dXuL7NZsNmOz+hXVFRUU0PQxCEBuzOblGk5pv5ZJ2nRcULOwXOIqJaNsFus1MsW8psU1H+FqVSSWR4OAqFgi3ff0VxUSF2i5mf5s7Cdngvap0OtVaLWqdHrdWh0mhQaTQcP2EnSF/+dA0FZgfh/g0r8aAgXK2qHbDs3buX7t27Y7VaMRgMLF68mFatWgGwcOFCbr/9doKCglCpVHh5ebF48WKaN29eYXmJiYnMnz+f+Ph40tPTefXVV+nduzf79u3Dx6f8TJBvvPFGmX4vgiBcfSRJYtLglqhkBx+uP4VGcqJGRaDKh7zQIIozU0vWLbZa+XxbEu8/+ywrli8vdzh0xqkUFixYQMqJ4zicLmLCQmkdEUra0UM4bVYcVisOmw2HzYrb5QLgUKOh+JUzy7XD5cZoc6JVidFBgnA5VDtgiY+PJykpicLCQn744QfGjRvH+vXradWqFa+88goFBQWsWrWK4OBglixZwm233caGDRto27ZtueWd67AL0K5dOxITE4mJiWHhwoU88MAD5W4zZcoUJk48n2SqqKiIqKi6m0dEEIT6NXFIW7oG2lm/egUup5sfWsZx9Nh+EpxO0goKeXjLHk65JY7Nfh/F9YN5eecBTmxL4lCxiYfXbqaZtxfBWjWbT2XT+dnJrBs6gKMpJ1m/fj33TJ6MVlv2lo7L6cRpt7Nw+k/EaOQy73/79ynMdicDEsrPQCwIQu2qdsCi0WhKWkw6d+7Mtm3b+OCDD5g0aRIff/wx+/bto3Xr1gC0b9+eDRs2MGvWLObMmVOl8v39/YmLi+PYsWMVrqPVasv9ghEE4erVO7EzXdu3ZteuJD6xaDHKCtYXmOi06wRofdDu3IJi7068/+971qvVWAyBuCQFKx1KfjG6wWSHsKYQBoFhYfSNiWXhwoW8/fbbvPzyy2X2p1SpUKpUmCQdweWkbfltTzr9W4bSMqzsHEOCINS+S27LdLvd2Gw2zGazp0BF6SKVSiVud/mJn8pjNBpJTk4mPDz8UqsmCMJVxtPP7Toy+ndkVND5ock97UbuLUzDnZVB4a03kjW6H8Uf/QfniWSKxw5lfawfm9pFc4slF53Ngk6rA8DhcHD06NEK9+dyujBJWkJ9S/dTSSuwsP1kPn3jReuKIFwu1QpYpkyZwp9//klKSgp79+5lypQprFu3jrvuuouWLVvSvHlzHnnkEf7++2+Sk5N55513WLlyJaNHjy4pY8CAAXz88cclr59//nnWr19PSkoKmzZt4qabbkKpVHLnnXfW2kEKgnD1cDqdWK1WmnjpSPDSonDYmNu7My88/zxHjhxh+vc/EPTpd3iPfwy/mBiSkpJoHhND0elTJG//G+/iApxOJ7/99hvffPMNgwYNqnBf6dn5yJKCRgHnm1hMNieTf9xDkLeGUR0iLschC4JANW8JZWVlce+995Keno6fnx/t2rVj+fLl3HjjjQD8/vvvvPjii4wYMQKj0Ujz5s354osvGDp0aEkZycnJ5OTklLxOTU3lzjvvJDc3l5CQEHr16sWWLVsIaWCzzwqC0DD8M38LK5dzY89e7Nq4AV9fX6ZERvKoxcqLR3exxdeHyMhIwBPobP1qHsVnzhD0oIrY2Fjeffddxo4dC3iy1uaePsmpfbsxFRbwzRE7yyyegGRNusyxxXs5mmVk/5lCHG6Zz8Z1wVdX/ughQRBqnyTLctneZFeYoqIi/Pz8KCwsxNdX3E8WhGuFW5ZpvXEfd4YHMa155a0dvX5eRYDDxtIxw3Da7eScPklWSjKn9+/l9P49mAryUapUaL0NfOl1Pek6z61ppSQRF+ZDk2Av2jT2Y0S7CKICver68AThqled67dIKSsIwhVLIUncHh7IvDPZ3B8ZTKSu4jl9zC43p7Xe+J04zDcvPUvmiWRktxtJUhDapBmt+g4gpk0HIlomoFJr6Hf4BA69DzGN/EVLiiA0ACJgEQThivZwZAiLMvIZvP0I90QE0dKgw6BUopYkMu0Ojptt/F1oYluhCbtWT2huBv5hEbTpdyOhTZoRHBWD+mwn3Au1bNm0Ho5GEISKiFtCgiBc8bJsDv5zIp1fsgoodpUelRiiUdHRx4s+gT709vMmzqAvN82+IAiXX3Wu3yJgEQThqiHLMkVOFyaXG4csE6xR4a1UVr6hIAj1QvRhEQThmiRJEn5qFX6iy4kgXHXEJBiCIAiCIDR4ImARBEEQBKHBEwGLIAiCIAgNnghYBEEQBEFo8ETAIgiCIAhCgycCFkEQBEEQGjwRsAiCIAiC0OCJgEUQBEEQhAZPBCyCIAiCIDR4ImARBEEQBKHBEwGLIAiCIAgNnghYBEEQBEFo8ETAIgiCIAhCg3dVzNYsyzLgmaZaEARBEIQrw7nr9rnr+MVcFQFLcXExAFFRUfVcE0EQBEEQqqu4uBg/P7+LriPJVQlrGji3201aWho+Pj5IkkRRURFRUVGcPn0aX1/f+q6eUA5xjho+cY4aPnGOGjZxfionyzLFxcVERESgUFy8l8pV0cKiUCiIjIwss9zX11f8kjRw4hw1fOIcNXziHDVs4vxcXGUtK+eITreCIAiCIDR4ImARBEEQBKHBuyoDFq1Wy/Tp09FqtfVdFaEC4hw1fOIcNXziHDVs4vzUrqui060gCIIgCFe3q7KFRRAEQRCEq4sIWARBEARBaPBEwCIIgiAIQoMnAhZBEARBEBq8qy5gOXLkCKNGjSI4OBhfX1969erF2rVrS60jSVKZx3fffVdPNb72VOUcnZObm0tkZCSSJFFQUHB5K3oNq+wc5ebmMnjwYCIiItBqtURFRTFhwgQxn9dlUtn52b17N3feeSdRUVHo9XoSEhL44IMP6rHG156qfM899dRTdO7cGa1WS4cOHeqnoleQqy5gGT58OE6nkzVr1rBjxw7at2/P8OHDycjIKLXevHnzSE9PL3mMHj26fip8DarqOQJ44IEHaNeuXT3U8tpW2TlSKBSMGjWKX375hSNHjjB//nxWrVrFo48+Ws81vzZUdn527NhBaGgoX3/9Nfv372fq1KlMmTKFjz/+uJ5rfu2o6vfc/fffz+23315PtbzCyFeR7OxsGZD//PPPkmVFRUUyIK9cubJkGSAvXry4HmooVPUcybIsf/LJJ3Lfvn3l1atXy4Ccn59/mWt7barOObrQBx98IEdGRl6OKl7Tanp+Hn/8cblfv36Xo4rXvOqeo+nTp8vt27e/jDW8Ml1VLSxBQUHEx8fz5ZdfYjKZcDqdzJ07l9DQUDp37lxq3SeeeILg4GC6devG559/XqWprYVLV9VzdODAAf71r3/x5ZdfVjohllC7qvN3dE5aWho//fQTffv2vcy1vfbU5PwAFBYWEhgYeBlreu2q6TkSKlHfEVNtO336tNy5c2dZkiRZqVTK4eHh8s6dO0ut869//UveuHGjvHPnTvnNN9+UtVqt/MEHH9RTja89lZ0jq9Uqt2vXTv7qq69kWZbltWvXihaWy6wqf0eyLMt33HGHrNfrZUAeMWKEbLFY6qG2156qnp9z/vrrL1mlUsnLly+/jLW8tlXnHIkWlqq5Iv51ffHFF8vtKHvh49ChQ8iyzBNPPEFoaCgbNmzg77//ZvTo0YwYMYL09PSS8l555RV69uxJx44dmTx5MpMmTeLtt9+uxyO88tXmOZoyZQoJCQncfffd9XxUV5fa/jsCeO+999i5cyc///wzycnJTJw4sZ6O7spXF+cHYN++fYwaNYrp06czcODAejiyq0ddnSOhaq6I1PzZ2dnk5uZedJ2mTZuyYcMGBg4cSH5+fqmpvFu0aMEDDzzAiy++WO62v/32G8OHD8dqtYo5H2qoNs9Rhw4d2Lt3L5IkASDLMm63G6VSydSpU3n11Vfr9FiuVnX9d7Rx40Z69+5NWloa4eHhtVr3a0FdnJ8DBw7Qr18/HnzwQWbOnFlndb9W1NXf0IwZM1iyZAlJSUl1Ue2rhqq+K1AVISEhhISEVLqe2WwGKNPnQaFQ4Ha7K9wuKSmJgIAAEaxcgto8Rz/++CMWi6XkvW3btnH//fezYcMGmjVrVou1vrbU9d/RufdsNtsl1PLaVdvnZ//+/fTv359x48aJYKWW1PXfkHBxV0TAUlXdu3cnICCAcePGMW3aNPR6PZ9++iknTpxg2LBhACxdupTMzEyuu+46dDodK1eu5PXXX+f555+v59pfG6pyjv4ZlOTk5ACQkJCAv7//5a7yNacq5+j3338nMzOTrl27YjAY2L9/Py+88AI9e/YkNja2fg/gKleV87Nv3z769+/PoEGDmDhxYslQWqVSWaULrnBpqnKOAI4dO4bRaCQjIwOLxVLSwtKqVSs0Gk091b4Bq7/uM3Vj27Zt8sCBA+XAwEDZx8dHvu666+Tff/+95P0//vhD7tChg2wwGGRvb2+5ffv28pw5c2SXy1WPtb62VHaO/kl0ur38KjtHa9askbt37y77+fnJOp1ObtGihTx58mRxji6Tys7P9OnTZaDMIyYmpv4qfY2pyvdc3759yz1PJ06cqJ9KN3BXRB8WQRAEQRCubVfEKCFBEARBEK5tImARBEEQBKHBEwGLIAiCIAgNnghYBEEQBEFo8ETAIgiCIAhCgycCFkEQBEEQGjwRsAiCIAiC0OCJgEUQBEEQhAZPBCyCIAiCIDR4ImARBEEQBKHBEwGLIAiCIAgNnghYBEEQBEFo8P4f1RIRz1mdQHAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Make a tentative set / list of border units. Numbered units have zero nonborder neighbors; will be surrounded next block\")\n",
    "print(\"'pop' shows pop/aDP for pop/aDP > 0.5 in a border unit\")\n",
    "isBorder = [0]*nUnits\n",
    "borderSet = set()\n",
    "for v in borderVTDs:\n",
    "    u = tractUnitNo[v]\n",
    "    borderSet.add(u)\n",
    "    isBorder[u] = 1\n",
    "borderUnits = list(borderSet)\n",
    "surroundedBorderUnits = list()\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "    if len(set(unitNbrs[u]).difference(set(borderUnits)) ) == 0:\n",
    "        plotCenter(u,unitGeom[u],9)\n",
    "        surroundedBorderUnits.append(u)\n",
    "    if unitPop[u] > 0.5 * aDP:\n",
    "        plotCenter(\"pop\"+str(r3(unitPop[u]/aDP)),unitGeom[u],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "1be34afe-7873-48d9-9b13-e8b48caee20b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a couple of these are big corners, not truly surrounded. Add a cattycorner neighbor\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cornerUnitSet = set() \n",
    "print(\"a couple of these are big corners, not truly surrounded. Add a cattycorner neighbor\")\n",
    "for u in surroundedBorderUnits:\n",
    "    for uu in range(nUnits):\n",
    "        if uu not in unitNbrs[u] and uu != u and unitGeom[u].intersects(unitGeom[uu]):\n",
    "            unitNbrs[u].append(uu)  #this works as if a tiny connection existed across the four-square\n",
    "            unitNbrs[uu].append(u)\n",
    "            plotPoly(unitGeom[uu])\n",
    "            plotCenter(\"corner\"+str(uu),unitGeom[uu])\n",
    "            cornerUnitSet.add(u)  \n",
    "            plotPoly(unitGeom[u],2)\n",
    "            plotCenter(u,unitGeom[u])\n",
    "            for uuu in unitNbrs[u]:\n",
    "                plotPoly(unitGeom[uuu], 0.3)\n",
    "            plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "0f99211c-4b63-45af-b6df-af41b4627c7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will collapse unit 515 into unit 481\n",
      "we will collapse unit 487 into unit 475\n",
      "we will collapse unit 73 into unit 209\n",
      "we will collapse unit 76 into unit 209\n",
      "we will collapse unit 1678 into unit 1724\n",
      "we will collapse unit 143 into unit 85\n",
      "we will collapse unit 48 into unit 43\n",
      "we will collapse unit 625 into unit 485\n",
      "we will collapse unit 142 into unit 84\n",
      "we will collapse unit 371 into unit 353\n",
      "we will collapse unit 628 into unit 170\n",
      "we will collapse unit 571 into unit 423\n",
      "we will collapse unit 444 into unit 442\n"
     ]
    }
   ],
   "source": [
    "surroundedBorderUnits = list(set(surroundedBorderUnits).difference(cornerUnitSet))\n",
    "\n",
    "surrounderBorderUnits = list()\n",
    "for u in surroundedBorderUnits:\n",
    "    candidates = list()\n",
    "    for uu in set(unitNbrs[u]).difference(set(surroundedBorderUnits)):\n",
    "        candidates.append(uu)\n",
    "    uuDists = [unitCP[u].distance(unitCP[uu]) for uu in candidates]\n",
    "    uuu = candidates[uuDists.index(np.min(uuDists)) ]\n",
    "    print(\"we will collapse unit\",u,\"into unit\",uuu)\n",
    "    surrounderBorderUnits.append(uuu)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "4db9ba9b-cb0c-4a6e-8ebe-284f3e0b78c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "for u in surrounderBorderUnits:\n",
    "    if u in surroundedBorderUnits:\n",
    "        print(\"uhoh, we named\",u,\"as a surrounder but it was surrounded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "3cf86982-9153-4e34-abde-2016afcb2e31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "absorbing surrounded border units ...\n",
      "we now have 1982 total units.\n"
     ]
    }
   ],
   "source": [
    "oldList = [unitTractList[u].copy() for u in range(nUnits)] #safekeeping\n",
    "newCornerUnitList = list()\n",
    "print(\"absorbing surrounded border units ...\")\n",
    "for u in range(nUnits):\n",
    "    if u in surroundedBorderUnits:\n",
    "        uu = surrounderBorderUnits[surroundedBorderUnits.index(u)]\n",
    "        oldList[uu] += oldList[u]\n",
    "        oldList[u] = list()\n",
    "        \n",
    "unitTractList, unitPop = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if u not in surroundedBorderUnits:\n",
    "        oldUnitNo.append(u)\n",
    "        unitTractList.append(oldList[u])\n",
    "        unitPop.append(np.sum([tractPop[b] for b in oldList[u] ]))\n",
    "        if u in cornerUnitSet:\n",
    "            newCornerUnitList.append(len(unitPop)-1)\n",
    "nUnits = len(unitPop)\n",
    "print(\"we now have\",nUnits,\"total units.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "3edc4190-01a7-424d-9a46-a643cdfac213",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updating reverse bg assignments ...\n",
      "state pop = 11799448 = 11799448\n",
      "reassign unit nbrs based on v topology. We'll add back cattycorners in a later block\n",
      "build unit nbr list based on adjacent vtd's\n",
      "rebuilding geom for unit 0\n",
      "rebuilding geom for unit 500\n",
      "rebuilding geom for unit 1000\n",
      "rebuilding geom for unit 1500\n"
     ]
    }
   ],
   "source": [
    "print(\"updating reverse bg assignments ...\")\n",
    "tractUnitNo = [-999 for t in range(nTracts)]  #need to reset after surrounds\n",
    "for u in range(nUnits):\n",
    "    for v in unitTractList[u]:\n",
    "        tractUnitNo[v] = u\n",
    "print(\"state pop =\",np.sum(tractPop),\"=\",np.sum([np.sum([tractPop[v] for v in unitTractList[u] ]) for u in range(nUnits)]) )\n",
    "\n",
    "print(\"reassign unit nbrs based on v topology. We'll add back cattycorners in a later block\")\n",
    "unitNbrSet = [set() for u in range(nUnits)]\n",
    "print(\"build unit nbr list based on adjacent vtd's\")  #these are list-based, not geom-based\n",
    "for v in range(nTracts):\n",
    "    u = tractUnitNo[v]\n",
    "    for vv in vtdNbrs[v]:\n",
    "        uu = tractUnitNo[vv]\n",
    "        if uu != u  and uu >= 0:  #avoid assigning bg's relegated to unit -999\n",
    "            unitNbrSet[u].add(uu)\n",
    "unitNbrs = [list(unitNbrSet[u]) for u in range(nUnits)]\n",
    "\n",
    "unitGeom = list()\n",
    "for u in range(nUnits):\n",
    "    if u%500 == 0:\n",
    "        print(\"rebuilding geom for unit\",u)\n",
    "    geo = tractGeom[unitTractList[u][0]]\n",
    "    for b in unitTractList[u]:\n",
    "        if b != unitTractList[u][0]:\n",
    "            geo = geo.union(tractGeom[b])\n",
    "    unitGeom.append(geo)\n",
    "\n",
    "unitCP = [unitGeom[u].centroid for u in range(nUnits)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "fe6787ab-5361-49ba-b670-347a16db92d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "need to add back in the cattycorners\n",
      "connecting corner 122 to its catty 126\n",
      "connecting corner 436 to its catty 481\n"
     ]
    }
   ],
   "source": [
    "print(\"need to add back in the cattycorners\")\n",
    "for u in newCornerUnitList:\n",
    "    for uu in range(nUnits):\n",
    "        if uu not in unitNbrs[u] and uu != u and unitGeom[u].intersects(unitGeom[uu]):\n",
    "            unitNbrs[u].append(uu)  #this works as if a tiny connection existed across the four-square\n",
    "            unitNbrs[uu].append(u)\n",
    "            print(\"connecting corner\",u,\"to its catty\",uu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "37df254c-38b2-4eae-931e-044e0f8fc9c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "unitCountyNo = [countyNo[unitTractList[u][0]] for u in range(nUnits)]  #this may be off for county-straddling units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "e0545c19-3336-4bad-9acf-5eb90106ff6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the unsplittable units, between 50pct and 105% of a district or a corner border unit\n",
      "14 65198   starts in county 42\n",
      "85 63119   starts in county 8\n",
      "122 14456   starts in county 3\n",
      "155 94437   starts in county 20\n",
      "234 75892   starts in county 30\n",
      "268 103994   starts in county 8\n",
      "376 100057   starts in county 20\n",
      "389 62399   starts in county 49\n",
      "406 98589   starts in county 24\n",
      "432 70173   starts in county 24\n",
      "436 9990   starts in county 52\n",
      "474 66171   starts in county 42\n",
      "478 69419   starts in county 30\n",
      "486 60423   starts in county 42\n",
      "506 71718   starts in county 12\n",
      "515 113729   starts in county 17\n",
      "520 88840   starts in county 20\n",
      "550 60167   starts in county 56\n",
      "555 67552   starts in county 8\n",
      "576 59921   starts in county 56\n",
      "and FYI, which ones are 0.4 - 0.5 aDP?\n",
      "    1 50920 46\n",
      "    60 48959 44\n",
      "    100 53275 46\n",
      "    124 48163 51\n",
      "    127 48773 22\n",
      "    150 50225 17\n",
      "    159 57686 17\n",
      "    165 49175 17\n",
      "    281 50318 75\n",
      "    303 53610 82\n",
      "    304 49949 8\n",
      "    328 55581 47\n",
      "    351 56051 75\n",
      "    404 52584 47\n",
      "    434 49085 24\n",
      "    453 54918 22\n",
      "    493 48721 11\n",
      "    565 48563 17\n",
      "    578 56999 28\n"
     ]
    }
   ],
   "source": [
    "unsplittableUnits = list()\n",
    "print(\"Here are the unsplittable units, between 50pct and 105% of a district or a corner border unit\")\n",
    "for u in range(nUnits):\n",
    "    if (unitPop[u] < 1.05*avgDistrictPop and unitPop[u] > 0.5 * avgDistrictPop) or u in newCornerUnitList:\n",
    "        unsplittableUnits.append(u)\n",
    "        print(u, r3(unitPop[u]),\"  starts in county\",unitCountyNo[u])\n",
    "print(\"and FYI, which ones are 0.4 - 0.5 aDP?\")\n",
    "for u in range(nUnits):\n",
    "    if unitPop[u] < 0.5*avgDistrictPop and unitPop[u] > 0.4 * avgDistrictPop:\n",
    "        print(\"   \",u, r3(unitPop[u]),unitCountyNo[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "5add9c83-4d1d-41e3-8393-e31aab496c41",
   "metadata": {},
   "outputs": [],
   "source": [
    "date = \"15Dec\"\n",
    "nUnits = len(unitPop)\n",
    "unitNo = [u for u in range(nUnits)]\n",
    "onBorder = [0 for u in range(nUnits)]\n",
    "unitCPx, unitCPy = [unitCP[u].x for u in range(nUnits)], [unitCP[u].y for u in range(nUnits)]\n",
    "unitCountyNo = [countyNo[unitTractList[u][0]] for u in range(nUnits)]  #this may be off for county-straddling units\n",
    "cantSplit = [0]*nUnits\n",
    "for u in range(nUnits):\n",
    "    if u in unsplittableUnits:\n",
    "        cantSplit[u] = 1\n",
    "for u in borderUnits:\n",
    "    onBorder[u] = 1\n",
    "nbrDF = pd.DataFrame( {\"unitNo\":unitNo,\"centroid x\":unitCPx,\"centroid y\":unitCPy,\"unitPop\":unitPop,\"onBorder\":onBorder,\n",
    "                       \"cantSplit\":cantSplit,\"unitNbrs\":unitNbrs, \"unitCountyNo\":unitCountyNo,\"unitTractList\":unitTractList} )\n",
    "outname = STATE+str(nDistricts)+\"schoolUnitTopologies_\"+date+\".csv\" \n",
    "outpath = \"state_map_files/\"+outname\n",
    "nbrDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "fe6b3dde-10af-4e01-ab58-49df834d85bf",
   "metadata": {},
   "source": [
    "Big Ohio cities list, and their 2020 Census pops:\n",
    "Akron - 190,469.   Enforce 0% or 60%+.  My calcn nearly on target\n",
    "Cincy - 309,317. Enforce 0% or 80%+   My calcn nearly on-target.\n",
    "Cleveland - 372,624 = 3 whole districts.  I count 417,000 which likely includes crannies. --> sprawling, enforce > 0.2\n",
    "Columbus - ~800,000 in Franklin County - seven whole districts but sprawling.  10 or 11 reasonable, no restrictions\n",
    "Dayton - 137,644.  Enforce 0% or 20%+  My calcn nearly on target\n",
    "Toledo - 270,871. No restriction.   My calcn nearly on target\n",
    "--------\n",
    "Parma 81,146 -- I AM OVERCOUNTING, but ok to keep whole  (note, these are from old by-precinct-name method)\n",
    "Canton 70,872 -- I AM OVERCOUNTING, but ok to keep whole\n",
    "Youngstown 67,296 -- I am undercounting (60,059)\n",
    "Lorain 63,468  -- about right\n",
    "Hamilton 61,932  -- about right\n",
    "Green 60,424 - about right  #Census has several CDP's inside Green\n",
    "West 8 = West Chester 64,901 -- about right\n",
    "not Springfield 57,719\n",
    "not Fairfield 42,000 -- we split out Fairfield township :-)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "78564573-ce6d-4d0e-b2d4-3c33f25b0be6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pull in the any-split Home Districts ...\n",
      "Now convert (approximately) to full VTD lists. We didn't store the VTDfrag lists, so use binary in-out if frac > 0.5\n",
      "Lets compare our true HDpops to this partial-based calculation\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#10/27/24\n",
    "print(\"Pull in the any-split Home Districts ...\")\n",
    "infile = \"./2024state_HD_output/OH8941legisl60_1p5PatchedVTDs.csv\"\n",
    "HDdf = pd.read_csv(infile)\n",
    "HDvtdListString, partialString, fracString = HDdf[\"HDvtdList\"], HDdf[\"partials\"],HDdf[\"HDvtdFrac\"]\n",
    "HDCPx, HDCPy, HDwt = HDdf[\"centroid x\"],HDdf[\"centroid y\"], HDdf[\"HDweight\"]\n",
    "HDvTruePop = HDdf[\"HDvPop\"]\n",
    "HDCP = [Point(HDCPx[v], HDCPy[v]) for v in range(nVTDs)]\n",
    "hdCP = HDCP.copy() #nomenclature\n",
    "nHDs = len(HDvtdListString)\n",
    "HDvtdList = [ast.literal_eval(HDvtdListString[h]) for h in range(nHDs)]\n",
    "HDpartials = [ast.literal_eval(partialString[h]) for h in range(nHDs)]\n",
    "HDvtdFrac = [ast.literal_eval(fracString[h]) for h in range(nHDs)]\n",
    "print(\"Now convert (approximately) to full VTD lists. We didn't store the VTDfrag lists, so use binary in-out if frac > 0.5\")\n",
    "for h in range(nHDs):\n",
    "    for i, frac in enumerate(HDvtdFrac[h]):\n",
    "        if frac >= 0.5:\n",
    "            HDvtdList[h].append(HDpartials[h][i])\n",
    "print(\"Lets compare our true HDpops to this partial-based calculation\")\n",
    "HDvtdPop = [np.sum ([ tractPop[v] for v in HDvtdList[h] ] ) for h in range(nHDs) ]\n",
    "popRatio = [(HDvtdPop[h] + 0.0001) / (HDvTruePop[h] + 0.0001) for h in range(nHDs) ]\n",
    "plt.scatter([h for h in range(nHDs)], popRatio, label=\"converted to true pop\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "#HDvtdFracList  = [list() for h in range(nHDs)]  ##modified 11/24 - do not use VTD fragments\n",
    "#for h in range(nHDs):\n",
    "#    for v in HDvtdList[h]:\n",
    "#        for f in VTDchildren[v]:\n",
    "#            HDvtdFracList[h].append(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "1fbef9d8-35f8-49cf-bb3e-6d6acf517033",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8941 119186.34343434343\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "print(nHDs, aDP)\n",
    "print(np.min(unitPop))\n",
    "minDistrictPop, maxDistrictPop = 0.95*aDP, 1.05*aDP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "b08d6302-63ee-451f-ae11-d61c5440f425",
   "metadata": {},
   "outputs": [],
   "source": [
    "unitVTDlist = [unitTractList[u].copy() for u in range(nUnits) ] #nomenclature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "9fe8940f-434d-405d-b52f-e17074715fcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now snap each HD to units.  Each HD is assigned its home unit, then we build out whole units, then ...\n",
      "... we add units that were partially included, biasing toward close, mostly full ones\n",
      "working on snapping HD 0 in unit 338 to whole units\n",
      "working on snapping HD 2002 in unit 194 to whole units\n",
      "working on snapping HD 4004 in unit 165 to whole units\n",
      "working on snapping HD 6006 in unit 412 to whole units\n",
      "HD 7708  has only 0.054 pop/aDP. We will swell by nearest munis to shore it up\n",
      "working on snapping HD 8006 in unit 512 to whole units\n"
     ]
    }
   ],
   "source": [
    "#10/27/24\n",
    "print(\"Now snap each HD to units.  Each HD is assigned its home unit, then we build out whole units, then ...\")\n",
    "print(\"... we add units that were partially included, biasing toward close, mostly full ones\")\n",
    "minSnapFrac, minEligFrac = 0.667, 0.1 #we auto-add contig munis at least this full in HDvtdList\n",
    "homeU, HDvPop, HDunitList = [-888]*nHDs, [0.]*nHDs, [list() for h in range(nHDs) ]\n",
    "popHDlist = list()\n",
    "for u in range(nUnits):\n",
    "    origArea = np.sum( [tractArea[v] for v in unitVTDlist[u] ] )\n",
    "    origL = origArea ** 0.5  #characteristic local district diameter\n",
    "    for v in unitVTDlist[u]:\n",
    "        homeU[v] = u\n",
    "for v in range(nHDs):\n",
    "    if HDvTruePop[v] > 0:\n",
    "        popHDlist.append(v)\n",
    "        \n",
    "for nh,h in enumerate(popHDlist):\n",
    "    if nh%2000 == 0:\n",
    "        print(\"working on snapping HD\",h,\"in unit\",homeU[h],\"to whole units\")\n",
    "    currPop, addedUset = unitPop[homeU[h]], { homeU[h] }\n",
    "    useFrac = [0.]*nUnits\n",
    "    for v in HDvtdList[h]:\n",
    "        useFrac[homeU[v]] += tractPop[v] / unitPop[homeU[v]]\n",
    "    shouldAddUset, hasFracUset = set(), set()\n",
    "    for u in range(nUnits):\n",
    "        if useFrac[u] > minSnapFrac :  #munis more than 2/3 captured should be snapped if contig path found\n",
    "            shouldAddUset.add(u)\n",
    "        if useFrac[u] > minEligFrac :  #munis that were at least 10% captured are eligible to be snapped\n",
    "            hasFracUset.add(u)\n",
    "    shouldAddUset = shouldAddUset.difference(addedUset)  #at this point, this knocks out just the home muni\n",
    "    nJustAdded = 1\n",
    "    while nJustAdded > 0 and currPop < aDP:  #this block -- add all mostly full HDs that are contig to growing list\n",
    "        nJustAdded = 0\n",
    "        canAddUset = set()\n",
    "        for u in addedUset:  #building all neighbors of in-HD munis\n",
    "            canAddUset = canAddUset.union(set(unitNbrs[u]) )\n",
    "        canAddUset = canAddUset.intersection( shouldAddUset.difference(addedUset) ) #whittling to eligible list\n",
    "        for u in canAddUset:\n",
    "            if currPop + 0.9 * unitPop[u] < maxDistrictPop: #prevent big overadds\n",
    "                currPop += unitPop[u]\n",
    "                addedUset.add(u)\n",
    "                nJustAdded +=1\n",
    "            if currPop > minDistrictPop:  #it's OK if we go over; we'll trim back later\n",
    "                break\n",
    "    # OK, we've added the 2/3 full munis.  Now add more contiguous partials if needed\n",
    "    eligibleSet = hasFracUset.difference(addedUset)\n",
    "    for u in list(eligibleSet):\n",
    "        if currPop + 0.9 * unitPop[u] > maxDistrictPop:\n",
    "            eligibleSet.remove(u)\n",
    "    contigAddableSet = set()  #the eligible units adjacent to the current (growing) set of units\n",
    "    for u in addedUset:\n",
    "        contigAddableSet = contigAddableSet.union(set(unitNbrs[u]).intersection(eligibleSet))\n",
    "    while currPop < minDistrictPop and len(contigAddableSet) > 0:  #add close, mostly used munis\n",
    "        canAddUlist = list(contigAddableSet)\n",
    "        canAddScores = [ unitCP[u].distance(HDCP[h])/origL * (1. - useFrac[u]) for u in canAddUlist]\n",
    "        addIdx = canAddScores.index(np.min(canAddScores))\n",
    "        u = canAddUlist[addIdx]\n",
    "        currPop += unitPop[u]\n",
    "        addedUset.add(u)\n",
    "        contigAddableSet.remove(u)\n",
    "        eligibleSet.remove(u)\n",
    "        contigAddableSet = contigAddableSet.union(set(unitNbrs[u]).intersection(eligibleSet))\n",
    "        for u in list(contigAddableSet):\n",
    "            if currPop + 0.9 * unitPop[u] > maxDistrictPop: #prevent big overadds\n",
    "                contigAddableSet.remove(u)\n",
    "    if currPop < 0.2 * aDP:\n",
    "        print(\"HD\",h,\" has only\",r3(currPop/aDP),\"pop/aDP. We will swell by nearest munis to shore it up\")\n",
    "        canAddUset = set(unitNbrs[homeU[h]])\n",
    "        for u in addedUset:\n",
    "            canAddUset = canAddUset.intersection(unitNbrs[u])\n",
    "        canAddUset = canAddUset.difference(addedUset)\n",
    "        while currPop < minDistrictPop and len(canAddUset) > 0:  #add close munis, biasing towards highly used\n",
    "            canAddUlist = list(canAddUset)\n",
    "            canAddScores = [ unitCP[u].distance(HDCP[h])/origL * (1. - useFrac[u]) for u in canAddUlist]\n",
    "            addIdx = canAddScores.index(np.min(canAddScores))\n",
    "            u = canAddUlist[addIdx]\n",
    "            currPop += unitPop[u]\n",
    "            addedUset.add(u)\n",
    "            canAddUset.remove(u)\n",
    "            for uu in unitNbrs[u]:\n",
    "                if useFrac[uu] > 0 and uu not in addedUset:\n",
    "                    canAddUset.add(uu)\n",
    "            for u in list(canAddUset):\n",
    "                if currPop + 0.9 * unitPop[u] > maxDistrictPop: #prevent big overadds\n",
    "                    canAddUset.remove(u)\n",
    "\n",
    "    HDvPop[h] = currPop\n",
    "    HDunitList[h] = list(addedUset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "6ca0e286-f4bc-4153-8e53-42e5b6757339",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lets plot our muni-snapped HD pops vs. orig pops\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and compute current unit usage with these whole-unit, possibly discontig HDs\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"lets plot our muni-snapped HD pops vs. orig pops\")\n",
    "plt.scatter(HDvtdPop, HDvPop)\n",
    "plt.axhline(aDP,ls=\"--\")\n",
    "plt.show()\n",
    "print(\"and compute current unit usage with these whole-unit, possibly discontig HDs\")\n",
    "unitUse = [0.]*nUnits\n",
    "for h in popHDlist:\n",
    "    for u in HDunitList[h]:\n",
    "        unitUse[u] += nDistricts * HDwt[h]\n",
    "plt.hist(unitUse,weights=unitPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "81a441d0-3a2f-473d-ba3b-0c8b365dfb55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAABX1ElEQVR4nO3deXxU5b0/8M9MlkkCZEKIMAFBIosSg2wWiFB7xXDZira2t1Zxw1YLwv2x9LpQReTSilbr0oobVrQXgWqrAkLjZVERLossUWKobEEQEmgSkoFAFjLP7494hkwyy3POnDNnZs7n/XrxesHkzMwzh8l5vud5vs/3sQkhBIiIiIhMYje7AURERGRtDEaIiIjIVAxGiIiIyFQMRoiIiMhUDEaIiIjIVAxGiIiIyFQMRoiIiMhUDEaIiIjIVIlmN0CGx+PBiRMn0KFDB9hsNrObQ0RERBKEEDhz5gy6du0Kuz3w+EdMBCMnTpxA9+7dzW4GERERaXDs2DFceumlAX8eE8FIhw4dADR/mPT0dJNbQ0RERDLcbje6d+/u7ccDiYlgRJmaSU9PZzBCREQUY0KlWDCBlYiIiEzFYISIiIhMxWCEiIiITMVghIiIiEzFYISIiIhMxWCEiIiITMVghIiIiEzFYISIiIhMFRNFz8h8TR6BHaVVOHWmDp07pGBoTiYS7NwniIiIwsdghEIqLC7D/NUlKKup8z6W7UzBvIm5GJuXbWLLiIgoHnCahoIqLC7D1KW7fQIRACivqcPUpbtRWFxmUsuIiCheMBiJAU0ega2HKrGy6Di2HqpEk0dE7H3nry6Bv3dTHpu/uiRi7SEiovjEaZooZ+YUyY7SqjYjIi0JAGU1ddhRWoX8Xp0MbQsREcUvjoxEMbOnSE6dCRyIaDmOiIjIHwYjUSoapkg6d0jR9TgiIiJ/GIxEKTVTJEYZmpOJbGcKAi3gtaF5ymhoTqZhbSAiovjHYCRKRcMUSYLdhnkTcwGgTUCi/HvexFzWGyEiorAwGIlS0TJFMjYvGy/fPhgup+/7uJwpePn2wawzQkREYeNqmiilTJGU19T5zRuxoTkgiMQUydi8bIzOdbECKxERGYLBSJRSpkimLt0NG+ATkJgxRZJgt3H5LhERGUL1NM2mTZswceJEdO3aFTabDR988EHI59TX1+ORRx7BZZddBofDgZ49e+KNN97Q0l5L4RQJERFZgeqRkdraWgwYMAD33HMPbr75Zqnn/OxnP8PJkyfx5z//Gb1790ZZWRk8Ho/qxloRp0iIiCjeqQ5Gxo0bh3HjxkkfX1hYiE8//RSHDx9GZmZzfkPPnj3Vvq2lcYqEiIjimeGraVatWoVrrrkGv//979GtWzf07dsX//Vf/4Xz588HfE59fT3cbrfPHyIiIopPhiewHj58GJs3b0ZKSgref/99VFRU4P7770dlZSWWLFni9zkLFy7E/PnzjW4aERERRQHDR0Y8Hg9sNhvefvttDB06FOPHj8ezzz6Lt956K+DoyJw5c1BTU+P9c+zYMaObSURERCYxfGQkOzsb3bp1g9Pp9D7Wr18/CCHw7bffok+fPm2e43A44HA4jG4aERERRQHDR0ZGjBiBEydO4OzZs97H9u/fD7vdjksvvdTotyciIqIopzoYOXv2LIqKilBUVAQAKC0tRVFREY4ePQqgeYrlzjvv9B5/2223oVOnTpg8eTJKSkqwadMmPPDAA7jnnnuQmpqqz6cgIiKimKU6GNm5cycGDRqEQYMGAQBmz56NQYMG4bHHHgMAlJWVeQMTAGjfvj3WrVuH6upqXHPNNZg0aRImTpyIP/7xjzp9BCIiIoplNiGEv61Poorb7YbT6URNTQ3S09PNbg4RERFJkO2/uWsvERERmYrBCBEREZmKwQgRERGZisEIERERmYrBCBEREZmKwQgRERGZisEIERERmYrBCBEREZmKwQgRERGZisEIERERmYrBCBEREZmKwQgRERGZisEIERERmYrBCBEREZmKwQgRERGZisEIERERmYrBCBEREZmKwQgRERGZisEIERERmYrBCBEREZmKwQgRERGZisEIERERmYrBCBEREZmKwQgRERGZisEIERERmYrBCBEREZmKwQgRERGZisEIERERmYrBCBEREZmKwQgRERGZisEIERERmYrBCBEREZmKwQgRERGZisEIERERmYrBCBEREZmKwQgRERGZisEIERERmYrBCBEREZmKwQgRERGZKtHsBlD8aPII7CitwqkzdejcIQVDczKRYLeZ3SwiIopyDEZIF4XFZZi/ugRlNXXex7KdKZg3MRdj87JNbBkREUU7TtNQ2AqLyzB16W6fQAQAymvqMHXpbhQWl5nUMiIiigUMRigsTR6B+atLIPz8THls/uoSNHn8HUFERMRghMK0o7SqzYhISwJAWU0ddpRWRa5RREQUUxiMUFhOnQkciGg5joiIrIfBCIWlc4cUXY8jIiLrYTBCYRmak4lsZwoCLeC1oXlVzdCczEg2i4iIYgiDEQpLgt2GeRNzAaBNQKL8e97EXNYbISKigBiMUNjG5mXj5dsHw+X0nYpxOVPw8u2DWWeEiIiCYtEz0sXYvGyMznWxAisREanGYIR0k2C3Ib9XJ7ObQUREMYbTNERERGQqBiNERERkKgYjREREZCrVwcimTZswceJEdO3aFTabDR988IH0c7ds2YLExEQMHDhQ7dsSERFRnFIdjNTW1mLAgAFYtGiRqudVV1fjzjvvxA033KD2LYmIiCiOqV5NM27cOIwbN071G02ZMgW33XYbEhISVI2mEBERUXyLSM7IkiVLcPjwYcybN0/q+Pr6erjdbp8/REREFJ8MD0YOHDiAhx9+GEuXLkViotxAzMKFC+F0Or1/unfvbnAriYiIyCyGBiNNTU247bbbMH/+fPTt21f6eXPmzEFNTY33z7FjxwxsJREREZnJ0AqsZ86cwc6dO7Fnzx5Mnz4dAODxeCCEQGJiIv73f/8Xo0aNavM8h8MBh8NhZNOIiIgoShgajKSnp2Pv3r0+j7300kvYuHEj/va3vyEnJ8fItyciIqIYoDoYOXv2LA4ePOj9d2lpKYqKipCZmYkePXpgzpw5OH78OP7yl7/AbrcjLy/P5/mdO3dGSkpKm8eJiIjImlQHIzt37sT111/v/ffs2bMBAHfddRfefPNNlJWV4ejRo/q1kIiIiOKaTQghzG5EKG63G06nEzU1NUhPTze7OURERCRBtv/m3jRERERkKgYjREREZCoGI0RERGQqBiNERERkKgYjREREZCpDi55RdGnyCOworcKpM3Xo3CEFQ3MykWC3md0sIiKyOAYjFlFYXIb5q0tQVlPnfSzbmYJ5E3MxNi/bxJYREZHVcZrGAgqLyzB16W6fQAQAymvqMHXpbhQWl5nUMiIiIgYjManJI7D1UCVWFh3H1kOVaPIErlvX5BGYv7oE/o5QHpu/uiToaxARERmJ0zQxRu10y47SqjYjIi0JAGU1ddhRWoX8Xp2MaDIREVFQHBmJIVqmW06dCRyIaDmOiIhIbwxGYoTW6ZbOHVKkXl/2OCIiIr0xGIkBTR6BN7eUSk+3tDQ0JxPZzhQEWsBrQ/M0z9CcTN3aS0REpAZzRqKcvxyRYFpPtyTYbZg3MRdTl+6GDfAZWVEClHkTc8OuN8IaJkREpBWDkSim5IioWefib7plbF42Xr59cJugxqVTnRHWMCEionDYhBBRv6bT7XbD6XSipqYG6enpZjcnIpo8AiOf2ig9ImJDc3Cx+aFRAUckjBi9CBQwKa/68u2DGZAQEVmUbP/NkZEoFWpJbkuBplv8BR96Lt8NlVRrQ3NS7ehcV5ugh9M6RESkYDASpdQstfU33RKJqROtNUw4rUNERC1xNU2Ukl1qO3dCP2x+aFSbQCQS5d+11DBhaXoiImqNwUiUkl2Se/eInDZTM5Eq/662hglL0xMRkT8MRiJIzZ4yypJcAG0CkmBLctVMnYRLbQ2TSLaNiIhiB3NGIkRLnoSWJbmRLP+utoYJS9MTEZE/DEYiINDyVyVPItjy17F52Rid65JeeRLp8u9qAiaWpiciIn8YjBgsnOWvigS7TXpJrjJ1Emg6RKlHomf5d9mASWlbeU2d3/NhRNuIiCj6MWfEYJHOk0iw23DjgODLY/Uo/+7vffN7dcJNA7shv1cnv6+vNQ+GiIjiG0dGDBapPAmliNi6knK8seVIwOPuuy6nzZSQlgJkWouWGV2anoiIYg+DEYNFIk9CdjM9G4BVX5ThwbH9vIGDlsTacIuWqc2DISKi+MZpGoOpXf6qVqAiYv60nhLSUoAs0HPKVBYtk5nWISIia2AwYrBw8iSaPAJbDlbgmY++xjMf/RNbDlT41CYJlhwbzKkzdZoKkIV6P+HnOURERKFwmiYCtORJFBaX4eH39qL6XKP3sRc/PoSMtCQ8eXN/jM3LVrWZXkudO6Ro2ldG5v387UVDREQUDIORCGmdJ5HVzgHYgIqz9dh6qNInZ6KwuAxTlu72+zrV5xoxZeluzCrog8raBlVtaLl09sMvT0g9p2VibXnNeannyB5HREQEMBiJKCVPorC4DP/1ty/8JoCOznXh8VUlIV/rufUHNLVBmRLSklhbJRn8yB5HREQEMBiJuFDVWGcW9EG525hy6C2X9Q7NyURGWpLPNFBrGWlJPom1me0dUu8jexwRERHABNaIkkkaXRKkRkg4lGW9apJLW6fUutLlRlNkjyMiIgIYjESUTNJo9fnAIxXhaL2sd0dpVdBREQA4fa7RpzKsskw5mHCWKRMRkTUxGIkg2cROZ2qSYW1QElJlK76uKyn3/l1ZpmyD/2XKNphbzr3JI7D1UCVWFh3H1kOVXGJMRBQjmDMSIYXFZViwZp/UsUZ2okpCqmwC6xtbjmBoTqY31yRay7mHWxWWiIjMw2AkAgIlrQZSW38BANAuOQG1DU26tMEGoEu6Ax4hsLLoOLLaO+BKd6DcXR/yea13FY62cu6hkoJfvn0wAxIioijGYMRgWqqkCjQHAR1SEvHK7UOwvbQKgECi3Y4XNhzwHqOGAFB3wYNJr2/3PpaRFno6yF/xM+DiMmWzhUoK9hdMERFRdGEwYjCtVVIFgHJ3PRIT7PivMVd4H78yu4PUpnj+tE5YrQmRwNpSuLsKK7Tu9huIlkqyREQUXRiMGCzcTrz181tOkZS767Dgw69QVattBY6a0ZVwdhVWGJHXIXt+9QqmiIhIf1xNY7BwO3F/z1emSH48qBue+HH/gDsC6yHcXYUVWnYIlqGlkiwREUUXBiMGk6nN4Y9sEDA2LxuLbhuEdskJGlvo+57+/j13Qi52lFZpXjKrZYdgWcr5DRSQ6RVMERGRcThNY7AEuw03DsjGq5tKpZ+jdKyhanY0eQRe3HgQS7aUhr3qZlZBH6z4/Fib5bo3DsjGgjXhTa0Ymdeh1D6ZunQ3bPCdepI9j0REZC4GIwZr8gis+iL4FITdBrQcFJCp2VFYXIaH39sbsopqKMpOvtNH9cH0UX18kktP19Zj2rI9YS+ZNTqvI1prnxARkRwGIwaTWU3jEcCPB3ZFt46pyL88C8N7dQp6J19YXIYpS3eH3TZ/IwfKyESTR2DkUxtDTq3ILJmNRF5HtNU+ISIieQxGDCZ7t/9+0QkAwN93Hw96N6/kX6iR/d10y6ovyqRHDmSCKNmpFSWvo7ymzm9wo4zOhJvXES21T4iISB0GIwZTe7cfagpEbd2S6df3xqzRfZFgt+HBsf2kRw7U7F0TKgBgXgcREQXD1TQGC7Xao7VQq0vU5lV0TEvCh1+ewNZDlQCap2FuGtgN+SGmgmSDqJVFJ6RWwSh5Ha5WK4tczhSWaycisjiOjBgs2KhAIMFWl6gZabHb4LM5n5pVMENzMpHZLilkQbXK2gbpVTDM6yAiIn84MhIBgUYFQvE3CqJmpKX1gIWaAmMJdht+PLCb5nYGe13Z0RkiIrIGBiMRMjYvG5sfGoXl9w7H9Ot7ST0nUPXVeRNzAbQtUhaK2gJjBbkuqddldVMiIgoHg5EISrDbMDQnE/m9spAWomJqRlpSwNUlWkdaAN8poNaaPAJbD1V6K60Ouayj5aqbtj4HWqrCEhGROswZiSB/G8UF0njBg22HKzH8cv9TGUr+xbZDlbj3f3binMoKrK2nVgJtYnfjgGy8tqnUEqtgjNjIj4iIQlM9MrJp0yZMnDgRXbt2hc1mwwcffBD0+Pfeew+jR4/GJZdcgvT0dOTn5+Ojjz7S2t6YFWijuEBqG5ow6fXtGLJgHV5Yf8DvHXqC3QbYoDoQAXynVoJtYvfaplLcd12OqlUwwUYXonXkwaiN/IiIKDTVIyO1tbUYMGAA7rnnHtx8880hj9+0aRNGjx6NJ554AhkZGViyZAkmTpyI7du3Y9CgQZoaHWuCbRQXSvX5Rjy3fj+W/F8pnry5f5vOX1myK6t1gbFQm9jZAKz6ogyfPnA9dn1zOuQqmGCjCwCicuRB5hzIVpslIiL1VAcj48aNw7hx46SPf/75533+/cQTT2DlypVYvXq1ZYIRtYXK/Kk+14gpS3fjlTajEepDnJZTK7Kb2O365jTye3VCk0dgR2kVPvzyRJugRBld8LeXTaDy9Wr3uTGCkRv5ERFRaBHPGfF4PDhz5gwyMwMnPdbX16O+vt77b7fbHYmmqaZ0zKFGC7RuAOfP46u+8rlDV3OnnpGW1GZ0Rc0mdsFGPUbnuoKOLgQSDSMPRm/kR0REwUU8GHnmmWdw9uxZ/OxnPwt4zMKFCzF//vwItko9NcmOWe0dur1vubseL248iBkFfdDkEfjr58ekn7vo1sEY0SfL5zHZZblHKmrx/PoDAXfwnVnQR/Poj8zIg2zgp0UkNvIjIqLAIhqMLFu2DPPnz8fKlSvRuXPngMfNmTMHs2fP9v7b7Xaje/fukWiilEDTEWXfTUf8YkRPFOS6LnaYOudoPrd+P65wtYczNRnl7vrQT0BzoDTcT0cvu4nd8h1Hg+ZULNlyRP4DBBBo5MHoVS6R2siPiIj8i1idkRUrVuCXv/wl3nnnHRQUFAQ91uFwID093edPtJBJRv3zliO4dfE2jHxqIwqLy1BRKxcwqDF/dQk+O/Av6eMDLcFNsNtw44DsoJ/n59/rETToEWhOtA2Xv5GHSKxyCVZILh6XMBMRRZuIBCPLly/H5MmTsXz5ckyYMCESb2kYNcmoSod5pOKc7u0oq6nDS58ckjp2VkGfgCMIhcVleG1TacDnXn/lJWhskls6nJacoLoqLBC4eFqoVS6AfDXZULiRHxGReVRP05w9exYHDx70/ru0tBRFRUXIzMxEjx49MGfOHBw/fhx/+ctfADRPzdx111144YUXMGzYMJSXlwMAUlNT4XQ6dfoYkaMmiVGZwljx+VG40h046a7Xe8YmJFe6A9NH9fH7syaPwOOrvgrapo3//Bc2/lNuBEapd+KvQJrw83fl34D/kYdIr3KJ5EZ+RubAEBHFGtXByM6dO3H99dd7/63kdtx111148803UVZWhqNHj3p//tprr+HChQuYNm0apk2b5n1cOT7WqE1iVDrMWQV98Pz6A9I79+ql7oIH60rK/d7Z/2nDAemcExk2AM60JKQkJqDcfTGIcAWpM+IKkvthxioXZSM/I7HSKxGRL5sQIjpKYAbhdrvhdDpRU1Njev5Ik0dg5FMbAyY7BvLCzwfCkWiXLgevF+Veu/VUw8K1JXg1yPRMON7+xTDY7Ta/d/1qRgS2HqrErYu3hXy/5fcOj5n6H4GSnwP9PxERxTLZ/pt706ikJDtOXbpb1SiH0vF2SEnC1kOVEELgzf87gloNpdzV8FfHY+2XJwwLRACgorYeNw3s5vdnakYe4m2VCyu9EhH5x117NVCza66SnHm6tgEjn9qISa9vx4sfH8SiTw4ZHogoWuZWNHkEHl1ZbOj76VWPI95WuajJgSEishIGIxqNzcvG5odGYfm9w3HPiJ4AAneYNw7IxrRl8pvkGeXUmeaOrqo2/GW4/gRaFROOeFrlwkqvRET+cZpGBwO6Z2BWQR8s33HUJyHU5UzB3An9sGDNvoivovHnSEVtWM9XElRrzjUHM7KrYsIVyVUuRmKlVyIi/xiMaORvRYQrPQWzCvqiZ1aat8MMd5O8dsl2CNi8y2YBoGNaUnOhsXMXRzjsNiBUuY3lO47i59/robktAPDkzf0BqFsVE654WQYbbzkwRER6YTCiQaAVESfddXh+/X68fPtgb5JmuEPu5xo8EGguXNYzq523Mwbg00FvP1yJ5zccCPpa5e76kMcEktkuCU/8+OIme5EaqYinZbDBkp9jMQeGiEgvzBlRSW1V0HCH3JXXfGvrEYzLy0Z+r05IsNu8q1JuGtgN+b06IeeSdmG9Tyhzf3iVT+ff+v2NCkSMLgUfafGUA0NEpBeOjKiktiro0JxMuNIdAYuL2QB0bJeMqtqGoO9bVduI4Qs34Ikf5/ntsIzOM3Cla399LdMs8bwMNl5yYIiI9MJgRCW1KyLWlZSj7oLH7zFK1/OjgV3xhsSut1W1DZiydDdmFfTF9FG9fTqvUPkIWoWbx6B1miXSpeAjLRKVXomIYgWnaVRSsyJi7ZdlmLJ0t0+iaUvOtCS8fPtgjM51qWrDc+v3Y8STG3ymKVrW5NCTADB3grY8hnCmWbgMlojIOhiMqHS6NvReLtnOFFSeqcP05buDHpealIDRuS7vqIaa7r7cXY8pS3fjv1d/ha2HKtHkEd58hIzUJBWvFNqjK/di7ZcnVD0n3B13uQw2fE0ega2HKrGy6Lj3O0JEFI04TaNCk0fgN++Hrl76w6uzMX1FUcjjWk4zKKss1HpjyxG8seWIz9RHh5QkTHp9u+rXSk2y43xj2ymlqtpG3L9sD371bTXmjM9Fk0dg26FKbD1cAaB5umH45b5JrOFOs3AZbHjiaRUSEcU/BiMq/HHDflSfD1299N2dx6RfU9ndVhnV+M37ezVVSFWmPpRpn2xniur6Jo6kBL/BiOLVTaXwCODdXd/6TD29+PFBZKQl4cmbLy79DXeahctgtQu09Lzld4QBCRFFE07TSCosLsMLGw5KHVt9/oL06y748Ctv7sTYvGxsm1OADinqY8SWUx9Acwl6WR3TkjDzht4Bc1taWvxZqd/jqs81YkqLPBA9plm4DFa9cKfHiIjMwJERCcoF3ghVtY3frZDpgx6d2qHqbD0Gdc/ApgMVql9Lmfp4ceNBvBZkV96rstNx9PQ5nKlrDppOn2vEm//3jdaP4ENZbqvXNAuXwaoT76uQiCg+MRiREG5JdxnPrddWGdWfxZ8dCrq896syd5vHZKafZPjLgwl3moXLYOVxFRIRxSJO00iItQv32fqm0AcZSDlfnGaJPK5CIqJYxJERCbxwq9PyfHGaJbK4ComIYhFHRiRoqQNiVdl+OrpI7GNDzVoWv2t9lrkKiYiiFYMRCcEu8PFo5g19kNku2eexbGcKfnVdTsjnsqMzH6fHiCjW2IQQUb/Gz+12w+l0oqamBunp6aa1w18hqXiiDOFvfmgUAPidWiksLsPD7+1ts7y3Y1oS7r42Bz2z0nSditGyyR4147kjIrPJ9t8MRlRq8ghsO1yJLQcrsPizw2hsivrTJ0VZ8TKroA96ZrUL2nm1rsCaaLdhxedHfXYm1qPap5FVRNlRExEZj8GIQeJ1dCQjrXk/m5YjHjIdf6Bqn0q3rnVawKjXVV6bpdKJiIwn238zZ0SFQLvQxrJ/65uFWQV9UXOusc3US6jddY2q9mlkFdFwdhImIiJjMBgJouWup1sOVuDxVV8FLSYWi+79fi+s+Pyopo5fTbVPNcJ53WA71bJUOhFRdGKdkQDidTqmJVe6A7BBc/lw2WJw60vKVVVQ1VpFNNT0C0ulExFFJ46M+BGP0zH+1F3wYMO+k1LH+gsQZIvBvV90XNVog5YqojLTLyyVTkQUnRiMtBJsKD/e1JxrxBtbjkgd6y9AGJqT2aYeiT9VtY2qpmpCFZmzwbe4muz0S1Y7h9T7s+IuEVFkMRhpJRKb4kULgeaOPdiK1tYdf0sJdht+NLCr1HupGW1QW0VUdvoFNqgKcoiIKDIYjLRitSF6AUCZQdFSPnx0rkvqfY5UnFPVLjVVRGX/zyrO1kddqfRgCbdERFbBBNbvKEWwDpw8a3ZTTPGLET2xtrjcZ4TBJVF7Q5lSCTWa9Pz6/bjC1V5VHQ/ZTfbU5Jjk9+qEl28f3CbRVeaz6o31ToiImrHoGayxciaUWQV9MH1Un5Adv7/KpetKyjFl6e6gr6+Umn/mpwNQUVuve8n4kU9tDLlT7eaHRnnfT00FViOqtRpZ1I2IKFqwAqukQJ2C1bjSHdjy8A0A/O9JAwS/k/+6/AyeW39A1XvqOQpgZCVYvUcvlOApUPDrL3giIopFsv23padprLRyJpRydz1e3HgAKz4/5rfjBeC3s1eWzt4zoqf69/zuuXqNAjjTktpUkc1IS8LCm/vrWpI+3Haz3gkRkS9LByNWWjkjw9/IhtLxOtOSAi6dtaG5lohaynPnry7B6FyX5lGAYKNbp1sFJ7JCLRcOp92sd0JE5MvSq2l4sQ9NfPen9YhD62OqahvR8bvN9tS+vpaS8YpQo1tK0KB2lYpRpe4BbUXdiIjimaWDEV7s9TX8cu31ObQGhkYFDUaOXqgt6kZEFO8sHYxUnqk3uwlxpdclHTQ/V2tgaFTQYOTohdqibkRE8c6ywUiTR+Cx1V+Z3Yy44UxNhDNV/TRNuKMARgUNyuhFMB3TkjS3W01RNyKieGfZBNYdpVWoqm0wuxlxo+b8Bfxu7T7YbRcruoaixyiAEjSEqjGiNmhIsNtw44BsvLqpNOAxp881Yl1JuebAQbaoGxFRvLPsyEh5zXmzmxCX1OSJ6jEKYNSUR2FxGV4LEogor68lObalBLsN+b064aaB3ZDfqxMDESKyJMuOjFSc5aiIPxmtanVktktCVa365bGtR0g6tUvGjQOy0TUjDdXnG2ADkH95FobrUEdDmfIIVeI9WCXVlj/LaufA46tC159hPRAiIn1YNhg5zSkaH650Bx6/8ao20wbl7jrM+muR6tfzCGDuhH44UX0e7xcdR2VtA5b83zc+x7z48SHdqrCGmvIIVkkVQFjbAXCJOBFReCwbjJzgNI1XUoINj47v5w0IWt7lbz1Uqfl1T1SfxxtbjgQdYdCzCqsy5dFasEqqofbUkcEl4kRE4bFszkjXjPjuQNRkHjQ2CUxfUYSFa0t8Hm/yCHg8AhkaVskAzVVZZaY6ALnciyaPwNZDlVhZdBxbD1VK5WqEqqQarnitB6LlXBMRaWXZkZH8nCy89Mlhs5thGJczBXMn9MOCNfsCrjRp7dVNpRhwaUeMvzo77J2M01MSpXNNZHIvtG5YZ3TJ/3isB2LE5oBERMFYdmTEnhBfHUhLme2SMHdCP4y/uqs3J0LW3JXFWPtl87RGOJ24u+6C6uf8ZesRv3fiyjRL6/YoUzyFxWUBX9OofI6MtCS8Eof1QMI510REWll2ZORUHFdfraptxP3L9uAlAOOv7oqXbx+Mh/++F9XnQ49UVNY24NGVxUFHUtKSE5CSlKB7nZZ/FJfjH8XlAC7eiY/OdYW1Yd2Rilpd25iRloTJ1+Zg+qjecTciYuTmgEREwVg2GNly4F9mN8Fw05fvwYuwYfzV2ejgSMKkP2+Xel6oIONcQxMW33EN7HYbyqrPY97qYpypa9KjyV7KnfjMgj7Se8+0nuIpLC7zuxOxWj8Z3A0j+1wCV3p8FyVTs88PlzITkZ4sOU3T5BFY/WX8Dzd7BHD/suah9RqJURE1Kmrrkd+rE7IzUnUPRICLyaVLthyROr71dIxyl6+Hv+8+jvmrv0LN+Ya4DUQAYzcHJCIKxpLByP8drED9BY/ZzYiY+atL8N8fyu3D0yElQeq4IxXnABjbMQlAamoJaLu8VjZxdVZBX7x02yCEijGqzzViSpznTBi5OSARUTCWDEbe3fWt2U2IqLKaOpS75XJkztQ1SS0LXvH5UTR5hHTH9NPB3TD9+l5Sx7aWkZoUsE2BNtqTDZJ6ZqWhYzuHdBn7cMu/R5PWy3eHXNYR2c4U1eeaiChclswZ+We52+wmRDWZrlbJHRhyWUepkvF/330ci24bFHRTu0Amj8jB8+v3w9aqbcH2nlFzl69mdCdeciYCLd+9cUA2XttUqupcExGFy5IjIx0cclMRFNzrnx3CD57+WKqeiADw3x+WYO4E/5va+aPciU8f1Rsv3z4YLqdvgNEl3YGZBX1Qf8HTZjmwspuvzF2+2mmHcKemzC4oFmz57mubSnHfdTltzrUemxoSEQWiemRk06ZNePrpp7Fr1y6UlZXh/fffx49+9KOgz/nkk08we/ZsfPXVV+jevTseffRR3H333RqbHL7R/VzYdbTGtPePFxv+qW5FUrm7HgdOncXLtw/G46u+kpo6Uu7EW+89c6TiHJbvOOqzWqZlYS5lN9+pS3eHvMtXAhfZuirh5EyYXVBMZvnuqi/K8OkD12PXN6f97vNDRKQ31SMjtbW1GDBgABYtWiR1fGlpKSZMmIDrr78eRUVFmDlzJn75y1/io48+Ut1YvVzV1Wnae1vdc+v3Y8/R0wg1NmK3Afddl+O3g/7iWDWeW78f5e7ghbmU3XxD3eUrgUuorjbcnIloKCgmu3x31zenkd+rE24a2A35vToxECEiQ6keGRk3bhzGjRsnffwrr7yCnJwc/OEPfwAA9OvXD5s3b8Zzzz2HMWPGqH17XVSd5469Znp1U2nIY4QAXttUikE9OmJsnlx5euVu/zfv78X5Rg9c6SkYnesKupuvQglcHn5vL6rPtZ12CjdnIloKilll+W6TR4T8Pyei6GF4AuvWrVtRUFDg89iYMWMwc+bMgM+pr69Hff3FIXy3W9+EUy5NjH4tO2iPR2Dasj3SSa9VtY2Y9dciAM0jGXMn5KJju+SQz1Omgl7ceABLthzxWVbsCnMqJVoKillh+a7ZU2FEpJ7hwUh5eTm6dOni81iXLl3gdrtx/vx5pKamtnnOwoULMX/+fMPaNLB7hmGvHUktcyFa50Xo8drQ+TXVUjroUOXpgymrqcP9y3b7PBasY0qw2zCjoC+mj+qj6511tIxIKPkxgVY02dAceEVq+a7eIxjKVFjrz6ZMhTEJlyg6ReVqmjlz5qCmpsb759ixY7q+/rLt3+j6emZxOVPwyu2D8dJtg9vc+YczIm23ATNu6GNqINKS7O6/smRyNBLsNl1zJqJlRELJjwECZ+1EavluYXEZRj61Ebcu3oYZK4pw6+JtGPnURs25M6GmwoD4qhNDFE8MD0ZcLhdOnjzp89jJkyeRnp7ud1QEABwOB9LT033+6OmbqnO6vp4ZOrVLxqcPXA+gOUei9X4y4VxvPQK44InfCrVmdExqlhobTcmPcaYltfmZv8eMYEQyr5qpMCKKLoYHI/n5+diwYYPPY+vWrUN+fr7Rbx3QZZlppr23XiprG/DyJwcxZelu6ZLpaoTTRacmNX+tojldMNIdU7ARCbMKivlL1K0512j4yh6jRjCiZSqMiNRTHYycPXsWRUVFKCoqAtC8dLeoqAhHjx4F0DzFcuedd3qPnzJlCg4fPowHH3wQ//znP/HSSy/hnXfewaxZs/T5BBrckd/TtPfW0yufHjLstWvONSAjVdtdcl1j86hK67vsbGcK0pKjq+BcqI5JzwJlskuNjRZsE8FIjBoZNYIRLVNhRKSe6gTWnTt34vrrr/f+e/bs2QCAu+66C2+++SbKysq8gQkA5OTkYM2aNZg1axZeeOEFXHrppXj99ddNW9YLAMmJdiTagVjfK+98o3EfYOl27Xk6ykqYlEQ73v7lMFScrUfnDinweAQm/Xm7bm1syfZdBq/a7jNYx2TEqozWxdvMWHZq9soeo0Ywoi05l4jkqQ5G/u3f/g1CBL7kv/nmm36fs2fPHrVvZais9kkod+s/vREpSQk2NDZFbyKeQHPF1a2HKjCi9yUYmpOJVV+cMO79vjsValYVdUxLCtgxBVqVUVZThylLd+Ol2wZh/NVdvY+rWRWiJMeaxezpDKNGMNRU3SWi6GLJjfKaPALu8xfMbkZYojkQaenFjw/hxY8PwZXuQN8uHQx9r3tG9MQ/isuly7qfPteIdSXlbUY5guU0KKYv34MXYcP4q/0XZIvmuhZmT2cYOYKhTIW1/v8It04MERnLksHIjtIqnGuMjc48XB1SEvHKpCHYergCL34cfo5JZrvkNit3ZJS766X2ognH6FwXHpmQix2lVVhfUo4/bzkS9PhAVU9DTWMAzSuO7l+2G7/6NgevbSqNqboWZk9nGD2CEQ1TYUSkTlTWGTHa+pJys5sQMT8dfClG9MnCrNFXBF1aKuvGAdlYfu9wPHfLQGS2M34ZaGa7JLx02yDpZbHKxndri0P/HwdKlFQzPbH4s7aBiPLaQHTWtYiGlT1GJ/PqXSeGiIxluZGRwuKykHfM8eTSjqlYWXQcnTs0l0Wf1qoaqVqrvyjD3B9ehQS7DalJdkxd2vx6RnW3c394FcZf3RV2u03qTrrJI/DmllLpqRqgbfChZnoiWJwRTiKo0XurRMN0BkcwiMwXLfs4WSoYCbakMR7ZbcCCNfu8/852puC+63Lw18+/1VybpLK2wdu5BurQ9ORKbw4MZDpPmc30/GkdfCjTGHp9JrWJoJHKQYmGYMDsZF4iK4umfDebCLY0Jkq43W44nU7U1NSEVY1166FK3Lp4m44tiy167V/zws8H4qaB3bz/ViLrzQf/hUU65KUAF/MWNj80yqdzDBTFB1r9Ekq2n/cAmn9JpywNbxRJsfze4dIdbqDPobQuGnNQiCj2ROpaI9t/W2pkxOqVF/WKOluPJCTYbag534C/7/pWp3do5i9vwd+dtMzql0DG57mw7XAlIICK2npvgDM2Lxsv3TYI05fvCTgVY0NzfZNgP1eTCCpTmfThv+9Fh5QkDL+ceRBEpE2oa02g5H4jWSoYYeXF8ATqXLWOSgRitwEv3ioflcusfgnkz1uOtMkhUoYpx1/dFS/C1mbXX+Di3cO9329eTQOEvypE5nNUn2/EpNe3R/XSYSKKbmYXPvTHUqtplFwAUi9Q5xrOqEQgHoE2uxAHo/eIV8vN2sZfnY1Xbh/c5nujrPqYMz5Xt1Uhaj5HOBvKEZG1mV340B9LjYwoSxr1ygWwkkCrLGRHJTqkJOJMnXyhOTW/BEcqaqWPldF6mDJUoqdeiaBqRu7MGkolothnduFDfywVjADw5gLcvyy6ytNHo45pSXhs4lVwpQfuXGWDBjWBCND8SyCz5KzJI7B8x9EAr6Jd62HK1rkqygZ6LdsW7nBmqGJkodpopmhZHkhEoZld+NAfywUjADAmLxtAbAcjypdFCIGT7npD6nycPtcIV3pK0I5O78hZ+Vyna+sx8qmNQZecKTVFjKzs6i/YMmo5XLDKpGrbqEa4gUQ0LQ80C4MxiiXRuI+TJYMRtVuTRyuliqbazkuNUB2d2rv5YJSv/Y0DsjFt2Z6gJdYBGFrfRNE62AqUrKtX+XcttVvCCQjDDSSMPh+xgMEYxaJoKHzYkqXqjChWFh3HjBVF4TfMJBlpSXjy5v7eL0ugi+HcCf1QVlPnU/hMLZkaGUqHBIQXEDW3ORcL1gTuiG0AnGlJqDnXaFjVV+V9uqQ78IefDUTF2eYlv0Mu64gfPP1x0Lb5q42iRZNHYNuhSkxbtjtggbpw3y/cOgNNHtFm9ErP9sUC1oWhWGf0qB7rjASR1c5hdhPCsujWwRjRJ8v772AJlCuLjmt6DzVzhuFUYnUk2vHkzf3hcqZiaE6m1JKz6nPy1WOVz/HMTwegorYeRyrO4fn1+72vFex96i54MOn17d7HMtsloao28HvrmcORYLdhRJ8sPPmT/n4DvXCHUvWoMxCNywMjKRprNRCpFS1VkC0ZjIS9W5yJXOkODPfzxQn0hcpqrz3wUtPRtQ6Idh6pwv9sC51YWn/Bg84t8lL0XErWssNuGbxd4WovFTi1DnqCBSIt6fkZjBpK1SOQiMblgZFk9WCMSE+WDEYqzhq7lb2RHr/xKukAobC4DI+v+kr1e7jSHXj8xqtUd3QtAyKPR0gFI0Bzmf4RvZuDBT0TYgN12K0Dp9J/1eL5DQd0e1+9k3qN2ENGj0AiGpcHRpLVgzEiPVkyGInFi2PrPJFQtFZFnVXQF9NH9Zbq6ILNNao7xxdbqVdC7NwJ/XD3iJyAn0MJnJS8Bz0YuRxO76FUPQKJaFweGElWD8aI9GTJYGRoTiYSbcCFKE7dHZ/nwuWXtAcgkH95Fob3kt+LREtVVLXZ/yFXEKi4ac+//OIUSqglZwLNgVmgBFalAwwWiLQUTil5fyK9HE4rPQKJaFweGElWD8biFZdpm8OSwUiC3Ybhl2di86HIL/H96eBLkd+rE363dh+qahsCHrf5YAVuuLIzunZMU/3LINvBPjK+HzqnO1T/wsks56y/4JF6rbTkhDY5MKNzXZhZ0BdLtpT6rCRRpl0A/8uZtXSAeg6hzyzoGzMrJ/QKJKJteWAkWT0Yi0dcpm0eSwYjAJDbLT3iwci93++JRyZcha2HKoMGIgDgrruAX//tSwDqfxlkO9jO6Q7cNLBbwJ/7u0MAILWC4Jn/GCDVhl9dd7nPxdrfxSAjNQmTR/TE9FF9vMcG6wBH57p8qqMOuawjdn1zWofppOB6ZqXp9lqRMDYvG4tuG4RHVxb7JOeqDSSMyGmJFVYOxuINa+aYy7LByCl38GDACIs/O4Ihl2VKjxooylT+Mugxlx3oDuHn3+sutYIAAiFzPzqmJWH6qD4+7+nvYlBzvhHPrz+AK1wdvJ8/UAe4rqS8Te0Lu615872Wn0PpKE7XNrT5uVaxlhtQWFyGBWv2+QQime2Sccs13VF/wYOthyqlg4poWR5oBisHY/GCy7TNZ9lg5NvT50x5XzWjBi0JyP8yhDuXHewO4bn1cqtOKmrrg5Y2twFYeHN/72fRcjFo3QEGanfrQEO507nvuhy8tqlUl8qxsZYbEOhcVdU2+Kws4hC1HCsHY/GAy7TNZze7AWYoLC7Dzm+qTXnvspo6eJoEMtsla3quTCl7ZS4baJtHGmouO1RQIKtzhxTvELbL6TtikO1MaTPKo+Zi4I+apF3x3Z/FnwUPROw2YMYNvfHCzwdiVkFf2KD+fKqlbMC3sug4th6qRJMeQzZ+3kP2XCmBW2Fxme7tIIoWXKZtPsuNjCgXYjP954o9AUt8hyL7y6B1Ljvc1SXKKMGQyzpi66FK1F/w4JmfDgBs8JZVbzmEreSl/EOyswv0+bW0O1Q/7xHA8MuzvHdC/oql6ZkbEKnkOTXnikPUZAVcpm0+ywUjei/l1EJrIAKo+2XQMpcdbuQv0LzRXes9XJROtfW0itoS8oE+v1F3LC1fV4/cgEDLBiOZPFfuVneuOERN8Y7LtM1nuWAklofZsjX8Mqidyz5SUau2WW28uqm0zWOtO1W1RdlCXQyMumNp/brh5AYE29BwwZp9EUmeKywuw4IP1VflBWL7d4coGC7TNp/lckZidZjNhuB5HnrkGRQWl4VMULWhOZdCLaVF81eXoOGCR1VRNpmLgXJno6Zpdlvg2mw2aAv+AlGCr9ajQOU1dbh/2Z6w8mXUtkF2j53WYvV3h0hGoBw3l58cN9Kf5UZGlE7L7KkaNYLlDeiVZyCbSyMACI05lUqn+j9bj6g6/zJ5GcHubFpTApB7v9+8msboOyG9koLDGZnQUpVXEY1D1GqqZLKiJsniMm3zWC4YUTqtKd9tyx5t5k7oh8z2DlSdrUdmu2S4nKkBfxnCyTNoeYHOau9AyYkaqQBhXF4X/KP4pJaP5vVNldyy6jvzL8O4vGzpi0GgpN3WdURaBjeDenQ0vGCVXnlK4YxMaG1DNA5RqwnAWVGT1OIybXNYLhgBmjut66+4BB9//a+wX2twdyf2HKsJu1YFAGS2S8Id+T2RnBh69qzJI/Dwe3s15RloSRxV9LqkA4DwgpHLMuUqlY7Ly1Z9UfB3ZxOsAmsk7oTCzbXQY2RCtg3OlETU1F3w/jvaKomqCcBZUZOiCUfogrNkMNLkEdj5zWldXuv7fS/BfT/ohYff24vqc9pXyQBAVW0jfvD0x1IX/xc3Hgj6foFWQGjdzVeR36sT/r77W0276iqd6h35PfH65lLDMtf93dkEC2paHm/EBUPNiIZRU0aybXjx1sFITLRH5QVTTWE8QG7bAi5XpkjgCF1olktgBZqHrM+0uPsLxwsbDgIAdj06GrMK+iAjNSngscolLyMt8DEyRaaaPAJLthyRal/LO+Jw8waynSkYfnmngAXVWh/v79/zJuYiOdGuuSibkQqLyzDyqY24dfE2zFhRhFsXb8PIpzaGXfArVHKtcm5fus245DnZBN8H/v4las434KaB3ZCvYqdoGUqi9fu7v8WfPzuM9/eoS7hWUxgv3CJ6RHoJlrzOgoIXWXJkpLzmvG6v1fIOa0ZBX0wf1Qc7SquwrqQcHxSd8NkQTxnyHnVlFwxfuMHvZnkyd207Squka5W0vCPWK29Ayc3wNxqUkZaEW665FKu+KAuah6GmKFug0Qo9RzFkhvS1TufILhscm5eNMXnGTBnJJviedBszhRFsalD2DtGIKplcrkxG4p438iwZjITaMVcN5Q5r2+FK2G02byfyyIRcPDIh12/HEmrX3lBFpmQvoBlpST5THVovvIHyBvxNE9Wca8Rrm0qx6LZB6NjOEbRTlcnXCDS8eeOA7DYBj9ZhT5kLxpz39uLxVV+h3F2v6f1kgy8jk+eUNrT+HC0ZcYEMNTUouxGkEVUyuVyZjMQ9b+RZMhjJbO/Q/TWnvb3bZ7Qis10SfntTHsZf3RWA7919YXG51GsGCh5kL6CTr83x6Uy0XHjnTuiHu0f4vk6wZcBKZ7ZgzT5sfmhUyBGMYJ1voE6srKZOqrCaLJkLxmk/gZfa94uGZYNj87LRISUJk17fHvAYPS+QaqYGQwVAaqtksqImmY173sizZDDiStf/bqj1tElVbSPuX7YHv/q22u/yURkHTp7xu417qIsy0DwqMn1Ub5/HZJ7XUrYzpU0gAqiL9mvON2hK3NKS36L1rl7rhUDL+0XDssGKs/5HRVrT4wIpOzUoEwCprZLJippkNu55I8+SCaxDczKDJprq6dVNpZjiJ3lJxosfH/KbRBlsV17Fkzf3b3OhlXleSzcOyPZ7sZbtpNaVlGtO3NKa36IlMTGcC0EsJkJG8gKpNqAJdbyaKpmsqElmk01e5widRUdGEuw2TB6Rg+fW7ze7KVL8zakHykEINepwMW+gJOSGaau+KMODY/u1CUhkO6kPik5oTtwK965czfPVjhiF+35mi+SmYGoDGpnj1Ux3RcPUGFkX97yRZ8lgBACmj+qNRR8fQEOTHuXKjCfQtvPWeqEdm5eNDo4kTPpz4LwBIPCwuUxn1rFdUlhJuuHelat5fssLRiTez0gyK4wieYGU3X5BbQCkZrpL7dQYi1ORP1q/F2pWDlqZZYORBLsNtw3tgTe3fmN2U6T567y15iBU1GrPG5DpzH48sBv+LFELJdCIQjijFXYbcDrA5wt2QXGmJbVZIdQxLQkCzauEoj0RUk1hpUAXyI7fJV7rdYFs+V0J9f8YDXeILE5F/oT7veAIXWiWzBlR3NCvi9lNUE2v6YBw8wZCzccXfFcFU+vrq81vackjgGnL9rTJSQlU1Gzh2hJMXbrb71Ll0+caccs1l/ptRySGWWV3ZNZSWGlsXjbmTshFZrtk72NVtY1YsGafroWYlO9KttP//3V2lORwsDhVfNBrF3OFXt8L5cbRiIKC8cAmhNY9WCPH7XbD6XSipqYG6enpurxmYXEZ/uvdL3C2vkmX14uU5fcO12U1RpNHYORTG0PmDSjLc4O9TqCCZHq8fqA7kh9enY0/by5FoOtM69cPtEw41A6/AOBKd+CxH+ZiwZp9Eb1jlr0bU851oKmQQOc62DkBEFahN3+U70p5zXlU1TYgs70DrnTz7xCbPALbDlVi2rLdAYsJyn5fyVx6j2xp/d2ii2T7b0sGI+Huz6KFTKcXSma7JHz+yGjdvvTKeQD8T7WEe7eq1+v7C3h2lFbh1sXbQj53+b3DMTQnM+gFRcasgr6YPqp3xIZZZQIF5dxtPVQpfS5a7sET6iKbkZYER6Jdc6G3WKB200i9bgZIf2p+Z2Rp+d0iX7L9t+WmacLZn0WtbGcKXrl9MF7xM52R7UzBrIK+eOHnA/H2L4ZJ1T757U15unZ+Ri991Ov1/Q1vqikmpHWZcEvPrd+PdSXlERlmDVURFmhOZlaGn7UUVpIt9Na6Ums8TVkEGn4PJpZWTUU7PadT1P7OyGLRssixXAKrHh2TrLkT+nk73FDD3Y/fGDzJ71fX5XiruerJ6MQqo15fTc6LXheKSO0hobaEtJb8n0gWetOTXitdtN6URMuqqVin93SKUWXXWbQsciwXjEQygl2wZh/G5DUXDgu16iXQ6oZO7ZKx4KY8jL/auGFxo6uCGvH6ampl6FWQLFJ7SKi9G9NSN0SvQm+RHJrWswNTe1MSTaumYp3MppRq/z+NGsGIZE0eq7PcNE0kI1i1lTnH5mVj80OjsPze4Xjh5wOx/N7h2PFIgaGBSKwKttqm9SqXUFUQ1YhEMKv2bkzNuVDocU4iGdjrvdJFTdtZnEo/Rk2nGDWCoeV3i7SxXDCiZ8ckQ+0Fm8u/5MnmpIS6oNgA/FAy4ItEMKulhLTa/Jxwlk4rIhXYG9GBqWk7y8frR810ihpGll3ntgKRYblpGj2qbarBuURjyeakhKqCODrXhZ1HTgcskR/J4VitFVLV5ucE21LgfGNT1BR6MyIfQGqzydQkLJo0GMMv502BXoyaTjG6qjCLlhnPcsEI0PzFmlnQ19C9aTiXGDmyOSmhLihKEjFg/h4SWktIq83PCXROlE0Oo2E/DSM6MJnO68mf9MeI3lnSr0mhGZkQanTZ9WjYcTueWTIYAYCeWWmGvTbnEqNXsAtKtO0hEam7MX/nJJrOhVEdWDR9RqswOiGUIxixy5JFzwD5YjbBZDtTMOSyjth8oMKncmO8FYayGm6UdlE0nAu9qvkGe32zP6OVGF1skaILK7CG0OQRuOa363Daz34kwXRMS8RjE/NwtLIWy3cc9SkKlZGahMkjcjB9VG9ezIh0xA4svnBDQuswtALrokWL0LNnT6SkpGDYsGHYsWNH0OOff/55XHHFFUhNTUX37t0xa9Ys1NWZW7EuwW7DzYO6qX7e737UH6lJdjy//kCb6pTV5xvx/HeVOil8em94RfqK5P+P2Ssa+F3Ul78yBpsfGsVAxMJU54z89a9/xezZs/HKK69g2LBheP755zFmzBh8/fXX6Ny5c5vjly1bhocffhhvvPEGrr32Wuzfvx933303bDYbnn32WV0+hFYFuS6pbe5b6pCShAf//mXADHwB4NfvfIEOjiQM59JczXjnFN3UbOKn1xSIWfkA/C4agwmh1JLqaZphw4bhe9/7Hl588UUAgMfjQffu3fGf//mfePjhh9scP336dOzbtw8bNmzwPvbrX/8a27dvx+bNm6Xe04hpGqD5QjlkwbqAO3X686OBXfFB0QmpY3nB0saIDa9IP7L/P/HQifO7SBQeQ6ZpGhoasGvXLhQUFFx8AbsdBQUF2Lp1q9/nXHvttdi1a5d3Kufw4cNYu3Ytxo8fH/B96uvr4Xa7ff4YIcFuw93X9lT1nHMNF6SPLYujTcUixagKjaQP2f+ftV+qr5gabVMh/C4SRY6qaZqKigo0NTWhS5cuPo936dIF//znP/0+57bbbkNFRQVGjhwJIQQuXLiAKVOm4De/+U3A91m4cCHmz5+vpmmafa+nuiVk3+vZCf9bckrVc8zaVCwWGbXhFelD9v/n0ZXFATtxfxvtReMoCr+LRJFjeDn4Tz75BE888QReeukl7N69G++99x7WrFmDBQsWBHzOnDlzUFNT4/1z7Ngxw9pXUVsf+qDvpCTacde1PZGRmiT9HK3lja2KW3ZHN9nzXlXbEPBnrX8n9N53Ri/8LhJFjqqRkaysLCQkJODkyZM+j588eRIul8vvc+bOnYs77rgDv/zlLwEA/fv3R21tLe677z488sgjsNvbxkMOhwMOh0NN0zRTUyjp+is7IznRjskjclRXb+UFSw637NYmUrUy9Dzvp87UhZwK8TeKEin8LhJFjqqRkeTkZAwZMsQnGdXj8WDDhg3Iz8/3+5xz5861CTgSEhIAANFQ4mTIZR3R3pEgdeztwy8DAEwf1RsZafKjIwAvWLKM3PAqXhUWl2HkUxtx6+JtmLGiCLcu3oaRT23UPKIQLHdD5v8ns53c70bnDimGbZymB34XiSJH9TTN7NmzsXjxYrz11lvYt28fpk6ditraWkyePBkAcOedd2LOnDne4ydOnIiXX34ZK1asQGlpKdatW4e5c+di4sSJ3qDELIXFZfjB0x/jbH1TyGPbORIw/PLmeeEEuw1P3txf6j14wVKHW3aro/cUR6jARub/57c35Ul34tE8FcLvIlHkqA5GbrnlFjzzzDN47LHHMHDgQBQVFaGwsNCb1Hr06FGUlV28AD766KP49a9/jUcffRS5ubn4xS9+gTFjxuDVV1/V71NoEOgiHkhSgu+pGpuXjVduH4xsZ+ARD16wtDG7wFWs0Hu1h2xgE+r/Z/zVXaU78WifCuF3kSgyLFkOXtnrQjYQUSy/d3ibrHllrn59STneLzqOqlp1e9RwX4zAeG6Ck91fyd/3trVQvxP+9n8J9f8js0LG6H1n9KLHd5HfZ7Ii2f7bkrv2hpqnDsTfULFSRTC/Vyf8ZkKuqotNNC5njCZWq9CotrPSc4pDyzLWUP8/MhVTlamQqUt3wwb/+85Ew8hiuN9F/q4TBWfJYETr/HOooWI1F6xAlR2VIXEOAVuLls5KzykOo3I3ZH4nlKmQ1p/fFeDzx9oIA3/XiUKzZDCiZf45s12Sbkmo0byckSJPa2elrPYINcUh8701O3dDdt+ZWBth4O86kRzDi55Fo1BL9vz5yeBuul0sonk5Y7SKtlLhegknCVXP1R7RsIxVGUW5aWA35PvZZDJai6MFw991IjmWDEaCXcQDef2zI20udlo7yGhezhiN9K6jEU3C7az0Wu0R7ctYY3WfGP6uE8mx5DQNEHieOpiWw6nhDBcbMSQea/PosuJ9vl2Pzkp2iiMUtbkbkRSr+8SYPf1FFCssG4wAzRffUVd2weAF/xuy8FnLi13N+YawOkg95/qB2JtHl2WF+Xa9Oiu9Vh7pFdjoLVZHGPT+XSeKV5acpmnp89IqqQqsinJ3XdjDxXoOicfiPLosK8y3R0OuRmuhcjfMEKsjDNE+/UUULSwfjCzdfkTV8VsO/EuXDlKPuf5YnUeXFat3w2po7aziNaE3kGgM2mSxiitRaJaepmnyCHy6/1+qnrN+3ymp42Q6yHCHxGN1Hl1WrN4Nq6U2VyNep+WCiZXiaIFE6/QXUbSwdDDy4saDONfgUfWc6vONoQ+CfAcZzlx/vI8cWGm+XU2djXhO6A0mmhNsZVitojCRGpYNRgqLy/Dc+v2anpuRmoSa842md5DxPnIQ63fDaoXqrKyQ0BsKRxiI4pMlc0aUi7pWk0f0BGB+Qlosz6PL4nz7RVZI6JURjQm2RBQeS46MaN0oD2ju3KeP6oMrXB1MHy62ysgB74abxfu0HAUXr7WEiACLBiPhXKznTuiHBLstajrIWJ9Hl8X59viflqPArJi0TNZiyWAknIt1x3YO79+jpYOMlsCIjGWlhN5YpvcIhpWTlsk6LBmMhLqoB1Pujs4h8GgJjMg4VpmWi2V6j2AwaZmswpIJrC0LTalVdbZe59YQydOS0Gu1AmlmMaIaMpOWySosOTICXLyo/+b9YlTVNkg/L7NdsoGtIgpNzbRcJHMNrJxgadQIBpOWySosG4wAzRf18w1NmPXOF9LPcTlTDWwRkRyZablI5hpYPcHSqGrITFomq7DkNE1LaoKLTu2SmRxIMSGS+xbF82aNsowawbBCLSEigMEIhuZkwpUud1ex4KY8yww7U2yLVK5BvG/WKMuoEQzu+ktWYflgJMFuw+M3hk5m/dV1ORh/dfwPN1N8iFSuARMsmxk5gsEqxGQFls4ZUYzNy8Yrtw/Gw+/tRfU5343w2jsS8PufXI3xV3c1qXVE6kUq14AJls2MXnbNWkIU7xiMfEf5Zd92uBJbD1UCEMi/PAvDufcFxaBIFUhjguVFRldDZi0himcMRlpIsNswoncWRvTOMrspRGGJVIE0VoX1xREMIm0snzNCFK8ikWvABMu2uKswkXo2IUTUp7m73W44nU7U1NQgPT3d7OYQRQXZImORKEZm9TojROSfbP/NYIQoBkVj52/lCqxE5B+DEQ14MaVYEKiyqvJN5XJPIooWsv03E1i/E413mkStcRdXIopHTGAFy1lT7GCRMSKKR5YPRljOmmIJi4wRUTyyfDDCO02KJSwyRkTxyPI5I7zTpHBEOumZRcaIKB5ZPhjhnSZpZUbSc6QqqxIRRZLlp2mUO81QTtc2RKA1FCvMTHrmLq5EFG9YZwTA2i9P4P5le4Iek+1MweaHRvGOk9DkERj51MaAuUbKVInR3xfWxSGiaMc6Iyp0bOcIeYySxMpdM0lN0rOR3xfu4kpE8cLy0zQAUF5zXtfjKL4x6ZmISF8MRgBUSeaDyB5H8Y1Jz0RE+mIwAiCzfehpGjXHUXw7XVsf8phsLq8lIpLGYASAK13uDlb2OIpfTR6BBWv2hTxu7gQuryUiksVgBHLLe3mnS0Do5FVFx3bJEWgNEVF8YDCCi4WkbLhYOEqhPMZCUgQweZWIyAgMRr7DQlIkI0tiGbia44iIiHVGfIzNy8boXBcLSVFgsl8FfmWIiKQxGGmFhaQomIqzoVfSqDmOiIg4TUOkCmuMEBHpjyMjRCooK6/Ka+rgb1MnZV+a1iuvuI8MEVFgDEaIVFBWXk1duhs2wCcgUUKL1iuvCovLMH91ic+S4GxnCuZNzGViNBEROE1DpJqalVeFxWWYunR3m9ok5TV1mLp0NwqLyyLSZiKiaMaRESINZFZeNXkE5q8u8TudI9A8kjJ/dQlG57o4ZUNElsZgpBXO7ZOsUCuvQlVrFQDKauqwo7SKK7iIyNI0TdMsWrQIPXv2REpKCoYNG4YdO3YEPb66uhrTpk1DdnY2HA4H+vbti7Vr12pqsJEKi8sw8qmNuHXxNsxYUYRbF2/DyKc2ciidNGG1ViIiOaqDkb/+9a+YPXs25s2bh927d2PAgAEYM2YMTp065ff4hoYGjB49GkeOHMHf/vY3fP3111i8eDG6desWduP1xLl90huXARMRyVEdjDz77LO49957MXnyZOTm5uKVV15BWloa3njjDb/Hv/HGG6iqqsIHH3yAESNGoGfPnvjBD36AAQMGhN14vYSa2wea5/abPP6OIPJPWQYcaJLPBm7ASEQEqAxGGhoasGvXLhQUFFx8AbsdBQUF2Lp1q9/nrFq1Cvn5+Zg2bRq6dOmCvLw8PPHEE2hqagqv5TpSM7dPJEtZBgz434AR4AaMRESAymCkoqICTU1N6NKli8/jXbp0QXl5ud/nHD58GH/729/Q1NSEtWvXYu7cufjDH/6A3/72twHfp76+Hm632+ePkTi3T0bhBoxERKEZvprG4/Ggc+fOeO2115CQkIAhQ4bg+PHjePrppzFv3jy/z1m4cCHmz59vdNO8OLdPRuIGjEREwakKRrKyspCQkICTJ0/6PH7y5Em4XC6/z8nOzkZSUhISEhK8j/Xr1w/l5eVoaGhAcnJym+fMmTMHs2fP9v7b7Xaje/fuapqqitYS30SyuAEjEVFgqqZpkpOTMWTIEGzYsMH7mMfjwYYNG5Cfn+/3OSNGjMDBgwfh8Xi8j+3fvx/Z2dl+AxEAcDgcSE9P9/ljJM7tExERmUf1aprZs2dj8eLFeOutt7Bv3z5MnToVtbW1mDx5MgDgzjvvxJw5c7zHT506FVVVVZgxYwb279+PNWvW4IknnsC0adP0+xQ64Nw+ERGROVTnjNxyyy3417/+hcceewzl5eUYOHAgCgsLvUmtR48ehd1+Mcbp3r07PvroI8yaNQtXX301unXrhhkzZuChhx7S71PohHP7REREkWcTQkR98Qy32w2n04mamhrDp2yIiIhIH7L9N3ftJSIiIlMxGCEiIiJTMRghIiIiUzEYISIiIlMxGCEiIiJTMRghIiIiUzEYISIiIlMxGCEiIiJTMRghIiIiU6kuB28GpUis2+02uSVEREQkS+m3QxV7j4lg5MyZMwCa97khIiKi2HLmzBk4nc6AP4+JvWk8Hg9OnDiBDh06wGbTb9M6t9uN7t2749ixY9zzRgOeP+147rTjuQsPz592PHfqCSFw5swZdO3a1WcT3dZiYmTEbrfj0ksvNez109PT+cUKA8+fdjx32vHchYfnTzueO3WCjYgomMBKREREpmIwQkRERKaydDDicDgwb948OBwOs5sSk3j+tOO5047nLjw8f9rx3BknJhJYiYiIKH5ZemSEiIiIzMdghIiIiEzFYISIiIhMxWCEiIiITGXpYGTRokXo2bMnUlJSMGzYMOzYscPsJhlq4cKF+N73vocOHTqgc+fO+NGPfoSvv/7a55i6ujpMmzYNnTp1Qvv27fGTn/wEJ0+e9Dnm6NGjmDBhAtLS0tC5c2c88MADuHDhgs8xn3zyCQYPHgyHw4HevXvjzTffbNOeWD7/Tz75JGw2G2bOnOl9jOcusOPHj+P2229Hp06dkJqaiv79+2Pnzp3enwsh8NhjjyE7OxupqakoKCjAgQMHfF6jqqoKkyZNQnp6OjIyMvCLX/wCZ8+e9Tnmyy+/xPe//32kpKSge/fu+P3vf9+mLe+++y6uvPJKpKSkoH///li7dq0xH1onTU1NmDt3LnJycpCamopevXphwYIFPnt98Pw127RpEyZOnIiuXbvCZrPhgw8+8Pl5NJ0nmbZYirCoFStWiOTkZPHGG2+Ir776Stx7770iIyNDnDx50uymGWbMmDFiyZIlori4WBQVFYnx48eLHj16iLNnz3qPmTJliujevbvYsGGD2Llzpxg+fLi49tprvT+/cOGCyMvLEwUFBWLPnj1i7dq1IisrS8yZM8d7zOHDh0VaWpqYPXu2KCkpEX/6059EQkKCKCws9B4Ty+d/x44domfPnuLqq68WM2bM8D7Oc+dfVVWVuOyyy8Tdd98ttm/fLg4fPiw++ugjcfDgQe8xTz75pHA6neKDDz4QX3zxhbjxxhtFTk6OOH/+vPeYsWPHigEDBoht27aJzz77TPTu3Vvceuut3p/X1NSILl26iEmTJoni4mKxfPlykZqaKl599VXvMVu2bBEJCQni97//vSgpKRGPPvqoSEpKEnv37o3MydDgd7/7nejUqZP48MMPRWlpqXj33XdF+/btxQsvvOA9huev2dq1a8Ujjzwi3nvvPQFAvP/++z4/j6bzJNMWK7FsMDJ06FAxbdo077+bmppE165dxcKFC01sVWSdOnVKABCffvqpEEKI6upqkZSUJN59913vMfv27RMAxNatW4UQzb/sdrtdlJeXe495+eWXRXp6uqivrxdCCPHggw+Kq666yue9brnlFjFmzBjvv2P1/J85c0b06dNHrFu3TvzgBz/wBiM8d4E99NBDYuTIkQF/7vF4hMvlEk8//bT3serqauFwOMTy5cuFEEKUlJQIAOLzzz/3HvOPf/xD2Gw2cfz4cSGEEC+99JLo2LGj91wq733FFVd4//2zn/1MTJgwwef9hw0bJn71q1+F9yENNGHCBHHPPff4PHbzzTeLSZMmCSF4/gJpHYxE03mSaYvVWHKapqGhAbt27UJBQYH3MbvdjoKCAmzdutXElkVWTU0NACAzMxMAsGvXLjQ2NvqclyuvvBI9evTwnpetW7eif//+6NKli/eYMWPGwO1246uvvvIe0/I1lGOU14jl8z9t2jRMmDChzefjuQts1apVuOaaa/Af//Ef6Ny5MwYNGoTFixd7f15aWory8nKfz+R0OjFs2DCfc5eRkYFrrrnGe0xBQQHsdju2b9/uPea6665DcnKy95gxY8bg66+/xunTp73HBDu/0ejaa6/Fhg0bsH//fgDAF198gc2bN2PcuHEAeP5kRdN5kmmL1VgyGKmoqEBTU5NPpwAAXbp0QXl5uUmtiiyPx4OZM2dixIgRyMvLAwCUl5cjOTkZGRkZPse2PC/l5eV+z5vys2DHuN1unD9/PmbP/4oVK7B7924sXLiwzc947gI7fPgwXn75ZfTp0wcfffQRpk6div/3//4f3nrrLQAXP3uwz1ReXo7OnTv7/DwxMRGZmZm6nN9oPXcA8PDDD+PnP/85rrzySiQlJWHQoEGYOXMmJk2aBIDnT1Y0nSeZtlhNTOzaS/qbNm0aiouLsXnzZrObEhOOHTuGGTNmYN26dUhJSTG7OTHF4/HgmmuuwRNPPAEAGDRoEIqLi/HKK6/grrvuMrl10e+dd97B22+/jWXLluGqq65CUVERZs6cia5du/L8Udyw5MhIVlYWEhIS2qx0OHnyJFwul0mtipzp06fjww8/xMcff4xLL73U+7jL5UJDQwOqq6t9jm95Xlwul9/zpvws2DHp6elITU2NyfO/a9cunDp1CoMHD0ZiYiISExPx6aef4o9//CMSExPRpUsXnrsAsrOzkZub6/NYv379cPToUQAXP3uwz+RyuXDq1Cmfn1+4cAFVVVW6nN9oPXcA8MADD3hHR/r374877rgDs2bN8o7Q8fzJiabzJNMWq7FkMJKcnIwhQ4Zgw4YN3sc8Hg82bNiA/Px8E1tmLCEEpk+fjvfffx8bN25ETk6Oz8+HDBmCpKQkn/Py9ddf4+jRo97zkp+fj7179/r8wq5btw7p6eneDic/P9/nNZRjlNeIxfN/ww03YO/evSgqKvL+ueaaazBp0iTv33nu/BsxYkSbJeT79+/HZZddBgDIycmBy+Xy+Uxutxvbt2/3OXfV1dXYtWuX95iNGzfC4/Fg2LBh3mM2bdqExsZG7zHr1q3DFVdcgY4dO3qPCXZ+o9G5c+dgt/teqhMSEuDxeADw/MmKpvMk0xbLMTuD1iwrVqwQDodDvPnmm6KkpETcd999IiMjw2elQ7yZOnWqcDqd4pNPPhFlZWXeP+fOnfMeM2XKFNGjRw+xceNGsXPnTpGfny/y8/O9P1eWp/77v/+7KCoqEoWFheKSSy7xuzz1gQceEPv27ROLFi3yuzw11s9/y9U0QvDcBbJjxw6RmJgofve734kDBw6It99+W6SlpYmlS5d6j3nyySdFRkaGWLlypfjyyy/FTTfd5HfJ5aBBg8T27dvF5s2bRZ8+fXyWXFZXV4suXbqIO+64QxQXF4sVK1aItLS0NksuExMTxTPPPCP27dsn5s2bF1VLU/256667RLdu3bxLe9977z2RlZUlHnzwQe8xPH/Nzpw5I/bs2SP27NkjAIhnn31W7NmzR3zzzTdCiOg6TzJtsRLLBiNCCPGnP/1J9OjRQyQnJ4uhQ4eKbdu2md0kQwHw+2fJkiXeY86fPy/uv/9+0bFjR5GWliZ+/OMfi7KyMp/XOXLkiBg3bpxITU0VWVlZ4te//rVobGz0Oebjjz8WAwcOFMnJyeLyyy/3eQ9FrJ//1sEIz11gq1evFnl5ecLhcIgrr7xSvPbaaz4/93g8Yu7cuaJLly7C4XCIG264QXz99dc+x1RWVopbb71VtG/fXqSnp4vJkyeLM2fO+BzzxRdfiJEjRwqHwyG6desmnnzyyTZteeedd0Tfvn1FcnKyuOqqq8SaNWv0/8A6crvdYsaMGaJHjx4iJSVFXH755eKRRx7xWVrK89fs448/9nuNu+uuu4QQ0XWeZNpiJTYhWpTxIyIiIoowS+aMEBERUfRgMEJERESmYjBCREREpmIwQkRERKZiMEJERESmYjBCREREpmIwQkRERKZiMEJERESmYjBCREREpmIwQkRERKZiMEJERESmYjBCREREpvr/u2c2KurnpJsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(unitPop,unitUse)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "0de7c528-dafc-45d4-ac1c-d2807c6cee55",
   "metadata": {},
   "source": [
    "for u in range(nUnits):\n",
    "    if unitUse[u] < 0.4:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "30de9de6-e831-4cfa-a449-2cc7bf3d330f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classify these muni HDs by contiguity\n",
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 0\n",
      "working on HD 1402 time is now 0\n",
      "working on HD 2102 time is now 1\n",
      "working on HD 2802 time is now 1\n",
      "working on HD 3503 time is now 2\n",
      "working on HD 4204 time is now 3\n",
      "working on HD 4906 time is now 3\n",
      "working on HD 5606 time is now 5\n",
      "working on HD 6306 time is now 5\n",
      "working on HD 7006 time is now 6\n",
      "working on HD 7706 time is now 8\n",
      "working on HD 8407 time is now 9\n",
      "out of 8934 total HDs, there were 8934.0 contiguous and 8591.0 complement-contiguous HDs\n",
      "0 HDs had both discontiguity problems, while enclave-only = 343 and discontig only= 0\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 0 343\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"classify these muni HDs by contiguity\") #take from vanillaHD\n",
    "\n",
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "6421f8ab-a699-4c29-976e-8c8e39d4597a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 154942 district pop vs 119186 target\n",
      "working on enclave-y HD 58 . We have evaluated 0 of 343 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 4791 . We have evaluated 200 of 343 enclavy HDs.Time is now 0\n",
      "Out of 343 HDs with enclaves 343 had enclaves.\n",
      "Of these, 293 wouldn't be over 154942 if all enclaves filled, while 50 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#copy enclave fix here.  Will not have any muni-discontig\n",
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.3 * aDP) #wider tolerance here for munis vs. vtd-based\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",int(maxPostFixPop),\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(eLgenerator):\n",
    "    if iii%200 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(eLgenerator),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(eLgenerator),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "aced004d-abcf-4b9d-8249-332156a1759a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 0.97893 0.13329\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjUAAAGdCAYAAADqsoKGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAz4ElEQVR4nO3df3RU9Z3/8VcSyCSQX0DMT6MBQcQiiSYmjdKmytRQsLusaQ1IC0011C2xwrQqsUikdDtUII1iao5dQfcskZQ9Sluk2YZYsC4RajDHCkIF1GBgAghJIGASkvv9wy9jpwRk4gxJPjwf59xT5jPv+5n3vVDnde7cHwGWZVkCAAAY4AL7ugEAAABfINQAAAAjEGoAAIARCDUAAMAIhBoAAGAEQg0AADACoQYAABiBUAMAAIwwqK8buFS6u7t18OBBhYeHKyAgoK/bAQAAF8GyLJ04cUIJCQkKDLzwsZjLJtQcPHhQSUlJfd0GAADohQMHDujKK6+8YM1lE2rCw8MlfbpTIiIi+rgbAABwMVpbW5WUlOT+Hr+QyybUnP3JKSIiglADAMAAczGnjnCiMAAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwAqEGAAAYgVADAACMQKgBAABGINQAAAAjXDZP6QZM1Nh8WsfbOnw+77ChwUqMCvX5vADgT4QaYIBqbD4t+4otOt3Z5fO5QwcHadOPswk2AAYUQg0wQB1v69Dpzi6V5qVqdEyYz+bde/ik5lXW63hbB6EGwIBCqAEGuNExYRqfGNnXbQBAn+NEYQAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwAqEGAAAYgVADAACMQKgBAABGINQAAAAj9CrUlJWVKTk5WSEhIcrMzNT27dvPW/ub3/xGX/nKVzRs2DANGzZMdrv9nHrLsrRo0SLFx8crNDRUdrtd7733nkfNsWPHNHPmTEVERCgqKkr33nuvTp482Zv2AQCAgbwONZWVlXI4HCouLtaOHTuUkpKinJwcHT58uMf6zZs3a8aMGfrzn/+s2tpaJSUl6Y477lBjY6O75oknntBTTz2l8vJybdu2TUOHDlVOTo4++eQTd83MmTO1c+dOVVdXa8OGDXrttdc0Z86cXmwyAAAwkuWljIwMa+7cue7XXV1dVkJCguV0Oi9q/TNnzljh4eHWCy+8YFmWZXV3d1txcXHWsmXL3DXNzc2WzWazXnzxRcuyLGvXrl2WJOuvf/2ru+aPf/yjFRAQYDU2Nl7U57a0tFiSrJaWlouqB/q7v33UbF39yAbrbx81D4h5AaA3vPn+9upITUdHh+rq6mS3291jgYGBstvtqq2tvag5Tp06pc7OTg0fPlyS9P7778vlcnnMGRkZqczMTPectbW1ioqKUnp6urvGbrcrMDBQ27Zt6/Fz2tvb1dra6rEAAABzeRVqjh49qq6uLsXGxnqMx8bGyuVyXdQcjzzyiBISEtwh5ux6F5rT5XIpJibG4/1BgwZp+PDh5/1cp9OpyMhI95KUlHRR/QEAgIHpkl79tHTpUq1du1Yvv/yyQkJC/PpZRUVFamlpcS8HDhzw6+cBAIC+Ncib4ujoaAUFBampqcljvKmpSXFxcRdcd/ny5Vq6dKk2bdqkCRMmuMfPrtfU1KT4+HiPOVNTU901/3wi8pkzZ3Ts2LHzfq7NZpPNZrvobQMAAAObV0dqgoODlZaWppqaGvdYd3e3ampqlJWVdd71nnjiCS1ZskRVVVUe58VI0siRIxUXF+cxZ2trq7Zt2+aeMysrS83Nzaqrq3PXvPrqq+ru7lZmZqY3mwAAAAzl1ZEaSXI4HJo9e7bS09OVkZGh0tJStbW1KT8/X5I0a9YsJSYmyul0SpJ++ctfatGiRaqoqFBycrL7HJiwsDCFhYUpICBA8+bN089//nONGTNGI0eO1GOPPaaEhARNmzZNkjRu3DhNnjxZBQUFKi8vV2dnpwoLCzV9+nQlJCT4aFcAAICBzOtQk5eXpyNHjmjRokVyuVxKTU1VVVWV+0TfhoYGBQZ+dgDomWeeUUdHh771rW95zFNcXKzHH39ckvTwww+rra1Nc+bMUXNzsyZOnKiqqiqP827WrFmjwsJCTZo0SYGBgcrNzdVTTz3Vm20GAAAGCrAsy+rrJi6F1tZWRUZGqqWlRREREX3dDvCFvdPYojtXvq4ND0zU+MTIfj8vAPSGN9/fPPsJAAAYgVADAACMQKgBAABGINQAAAAjEGoAAIARCDUAAMAIhBoAAGAEQg0AADACoQYAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwAqEGAAAYgVADAACMQKgBAABGINQAAAAjEGoAAIARCDUAAMAIvQo1ZWVlSk5OVkhIiDIzM7V9+/bz1u7cuVO5ublKTk5WQECASktLz6k5+94/L3PnznXXfO1rXzvn/fvvv7837QMAAAN5HWoqKyvlcDhUXFysHTt2KCUlRTk5OTp8+HCP9adOndKoUaO0dOlSxcXF9Vjz17/+VYcOHXIv1dXVkqRvf/vbHnUFBQUedU888YS37QMAAEN5HWpKSkpUUFCg/Px8XX/99SovL9eQIUO0atWqHutvvvlmLVu2TNOnT5fNZuux5oorrlBcXJx72bBhg6655hplZ2d71A0ZMsSjLiIiwtv2AQCAobwKNR0dHaqrq5Pdbv9sgsBA2e121dbW+qShjo4O/fd//7e+//3vKyAgwOO9NWvWKDo6WuPHj1dRUZFOnTrlk88EAAAD3yBvio8ePaquri7FxsZ6jMfGxmr37t0+aWj9+vVqbm7W9773PY/xe+65R1dffbUSEhL09ttv65FHHtGePXv00ksv9ThPe3u72tvb3a9bW1t90h8AAOifvAo1l8Jzzz2nb3zjG0pISPAYnzNnjvvPN9xwg+Lj4zVp0iTt27dP11xzzTnzOJ1OLV682O/9AgCA/sGrn5+io6MVFBSkpqYmj/GmpqbzngTsjQ8//FCbNm3Sfffd97m1mZmZkqS9e/f2+H5RUZFaWlrcy4EDB75wfwAAoP/yKtQEBwcrLS1NNTU17rHu7m7V1NQoKyvrCzezevVqxcTEaOrUqZ9bW19fL0mKj4/v8X2bzaaIiAiPBQAAmMvrn58cDodmz56t9PR0ZWRkqLS0VG1tbcrPz5ckzZo1S4mJiXI6nZI+PfF3165d7j83Njaqvr5eYWFhGj16tHve7u5urV69WrNnz9agQZ5t7du3TxUVFZoyZYpGjBiht99+W/Pnz9dXv/pVTZgwodcbDwAAzOF1qMnLy9ORI0e0aNEiuVwupaamqqqqyn3ycENDgwIDPzsAdPDgQd14443u18uXL9fy5cuVnZ2tzZs3u8c3bdqkhoYGff/73z/nM4ODg7Vp0yZ3gEpKSlJubq4WLlzobfsAAMBQvTpRuLCwUIWFhT2+949BRfr0bsGWZX3unHfcccd565KSkrRlyxav+wQAAJcPnv0EAACMQKgBAABGINQAAAAjEGoAAIARCDUAAMAIhBoAAGAEQg0AADACoQYAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwAqEGAAAYgVADAACMQKgBAABGINQAAAAjEGoAAIARCDUAAMAIhBoAAGAEQg0AADBCr0JNWVmZkpOTFRISoszMTG3fvv28tTt37lRubq6Sk5MVEBCg0tLSc2oef/xxBQQEeCzXXXedR80nn3yiuXPnasSIEQoLC1Nubq6ampp60z4AADCQ16GmsrJSDodDxcXF2rFjh1JSUpSTk6PDhw/3WH/q1CmNGjVKS5cuVVxc3Hnn/dKXvqRDhw65l9dff93j/fnz5+sPf/iD1q1bpy1btujgwYO66667vG0fAAAYyutQU1JSooKCAuXn5+v6669XeXm5hgwZolWrVvVYf/PNN2vZsmWaPn26bDbbeecdNGiQ4uLi3Et0dLT7vZaWFj333HMqKSnR7bffrrS0NK1evVpbt27VG2+84e0mAAAAA3kVajo6OlRXVye73f7ZBIGBstvtqq2t/UKNvPfee0pISNCoUaM0c+ZMNTQ0uN+rq6tTZ2enx+ded911uuqqq877ue3t7WptbfVYAACAubwKNUePHlVXV5diY2M9xmNjY+VyuXrdRGZmpp5//nlVVVXpmWee0fvvv6+vfOUrOnHihCTJ5XIpODhYUVFRF/25TqdTkZGR7iUpKanX/QEAgP6vX1z99I1vfEPf/va3NWHCBOXk5Gjjxo1qbm7Wb3/7217PWVRUpJaWFvdy4MABH3YMAAD6m0HeFEdHRysoKOicq46ampoueBKwt6KionTttddq7969kqS4uDh1dHSoubnZ42jNhT7XZrNd8BweAABgFq+O1AQHBystLU01NTXuse7ubtXU1CgrK8tnTZ08eVL79u1TfHy8JCktLU2DBw/2+Nw9e/aooaHBp58LAAAGLq+O1EiSw+HQ7NmzlZ6eroyMDJWWlqqtrU35+fmSpFmzZikxMVFOp1PSpycX79q1y/3nxsZG1dfXKywsTKNHj5Yk/eQnP9E3v/lNXX311Tp48KCKi4sVFBSkGTNmSJIiIyN17733yuFwaPjw4YqIiNADDzygrKwsffnLX/bJjgAAAAOb16EmLy9PR44c0aJFi+RyuZSamqqqqir3ycMNDQ0KDPzsANDBgwd14403ul8vX75cy5cvV3Z2tjZv3ixJ+uijjzRjxgx9/PHHuuKKKzRx4kS98cYbuuKKK9zr/epXv1JgYKByc3PV3t6unJwc/frXv+7tdgMAAMMEWJZl9XUTl0Jra6siIyPV0tKiiIiIvm4H+MLeaWzRnStf14YHJmp8YmS/nxcAesOb7+9+cfUTAADAF0WoAQAARiDUAAAAIxBqAACAEQg1AADACF5f0g3g8rD38Em/zDtsaLASo0L9MjeAyxuhBoCHYUODFTo4SPMq6/0yf+jgIG36cTbBBoDPEWoAP2tsPq3jbR0+n9dfR1ISo0K16cfZfut5XmW9jrd1EGoA+ByhBvCjxubTsq/YotOdXX6ZP3RwkIYNDfb5vIlRoYQOAAMOoQbwo+NtHTrd2aXSvFSNjgnz+fycnwIAnyHUAJfA6JgwHjkAAH7GJd0AAMAIhBoAAGAEQg0AADACoQYAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwQq9CTVlZmZKTkxUSEqLMzExt3779vLU7d+5Ubm6ukpOTFRAQoNLS0nNqnE6nbr75ZoWHhysmJkbTpk3Tnj17PGq+9rWvKSAgwGO5//77e9M+AAAwkNehprKyUg6HQ8XFxdqxY4dSUlKUk5Ojw4cP91h/6tQpjRo1SkuXLlVcXFyPNVu2bNHcuXP1xhtvqLq6Wp2dnbrjjjvU1tbmUVdQUKBDhw65lyeeeMLb9gEAgKEGebtCSUmJCgoKlJ+fL0kqLy/XK6+8olWrVmnBggXn1N988826+eabJanH9yWpqqrK4/Xzzz+vmJgY1dXV6atf/ap7fMiQIecNRgAA4PLm1ZGajo4O1dXVyW63fzZBYKDsdrtqa2t91lRLS4skafjw4R7ja9asUXR0tMaPH6+ioiKdOnXqvHO0t7ertbXVYwEAAOby6kjN0aNH1dXVpdjYWI/x2NhY7d692ycNdXd3a968ebr11ls1fvx49/g999yjq6++WgkJCXr77bf1yCOPaM+ePXrppZd6nMfpdGrx4sU+6QkAAPR/Xv/85G9z587VO++8o9dff91jfM6cOe4/33DDDYqPj9ekSZO0b98+XXPNNefMU1RUJIfD4X7d2tqqpKQk/zUOAAD6lFehJjo6WkFBQWpqavIYb2pq8sm5LoWFhdqwYYNee+01XXnllReszczMlCTt3bu3x1Bjs9lks9m+cE8AAGBg8OqcmuDgYKWlpammpsY91t3drZqaGmVlZfW6CcuyVFhYqJdfflmvvvqqRo4c+bnr1NfXS5Li4+N7/bkAAMAcXv/85HA4NHv2bKWnpysjI0OlpaVqa2tzXw01a9YsJSYmyul0Svr05OJdu3a5/9zY2Kj6+nqFhYVp9OjRkj79yamiokK/+93vFB4eLpfLJUmKjIxUaGio9u3bp4qKCk2ZMkUjRozQ22+/rfnz5+urX/2qJkyY4JMdAQAABjavQ01eXp6OHDmiRYsWyeVyKTU1VVVVVe6ThxsaGhQY+NkBoIMHD+rGG290v16+fLmWL1+u7Oxsbd68WZL0zDPPSPr0Bnv/aPXq1fre976n4OBgbdq0yR2gkpKSlJubq4ULF3rbPgAAMFSvThQuLCxUYWFhj++dDSpnJScny7KsC873ee8nJSVpy5YtXvUIAAAuLzz7CQAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwAqEGAAAYgVADAACMQKgBAABG6NWznwDTNDaf1vG2Dp/Pu/fwSZ/PCQDoGaEGl73G5tOyr9ii051dfpk/dHCQhg0N9svcAIDPEGpw2Tve1qHTnV0qzUvV6Jgwn88/bGiwEqNCfT4vAMAToQb4/0bHhGl8YmRftwEA6CVOFAYAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwAqEGAAAYgVADAACM0KtQU1ZWpuTkZIWEhCgzM1Pbt28/b+3OnTuVm5ur5ORkBQQEqLS0tFdzfvLJJ5o7d65GjBihsLAw5ebmqqmpqTftAwAAA3kdaiorK+VwOFRcXKwdO3YoJSVFOTk5Onz4cI/1p06d0qhRo7R06VLFxcX1es758+frD3/4g9atW6ctW7bo4MGDuuuuu7xtHwAAGMrrUFNSUqKCggLl5+fr+uuvV3l5uYYMGaJVq1b1WH/zzTdr2bJlmj59umw2W6/mbGlp0XPPPaeSkhLdfvvtSktL0+rVq7V161a98cYb3m4CAAAwkFehpqOjQ3V1dbLb7Z9NEBgou92u2traXjVwMXPW1dWps7PTo+a6667TVVdddd7PbW9vV2trq8cCAADM5VWoOXr0qLq6uhQbG+sxHhsbK5fL1asGLmZOl8ul4OBgRUVFXfTnOp1ORUZGupekpKRe9QcAAAYGY69+KioqUktLi3s5cOBAX7cEAAD8aJA3xdHR0QoKCjrnqqOmpqbzngTsiznj4uLU0dGh5uZmj6M1F/pcm8123nN4AACAebw6UhMcHKy0tDTV1NS4x7q7u1VTU6OsrKxeNXAxc6alpWnw4MEeNXv27FFDQ0OvPxcAAJjFqyM1kuRwODR79mylp6crIyNDpaWlamtrU35+viRp1qxZSkxMlNPplPTpicC7du1y/7mxsVH19fUKCwvT6NGjL2rOyMhI3XvvvXI4HBo+fLgiIiL0wAMPKCsrS1/+8pd9siMAAMDA5nWoycvL05EjR7Ro0SK5XC6lpqaqqqrKfaJvQ0ODAgM/OwB08OBB3Xjjje7Xy5cv1/Lly5Wdna3Nmzdf1JyS9Ktf/UqBgYHKzc1Ve3u7cnJy9Otf/7q32w0AAAwTYFmW1ddNXAqtra2KjIxUS0uLIiIi+rod9CPvNLbozpWva8MDEzU+MbKv2zEa+xqAt7z5/jb26icAAHB5IdQAAAAjEGoAAIARCDUAAMAIhBoAAGAEry/pBoAvau/hkz6fc9jQYCVGhfp8XgADB6EGwCUzbGiwQgcHaV5lvc/nDh0cpE0/zibYAJcxQg2ASyYxKlSbfpyt420dPp137+GTmldZr+NtHYQa4DJGqAFwSSVGhRI8APgFJwoDAAAjEGoAAIARCDUAAMAIhBoAAGAEQg0AADACoQYAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwQq9CTVlZmZKTkxUSEqLMzExt3779gvXr1q3Tddddp5CQEN1www3auHGjx/sBAQE9LsuWLXPXJCcnn/P+0qVLe9M+AAAw0CBvV6isrJTD4VB5ebkyMzNVWlqqnJwc7dmzRzExMefUb926VTNmzJDT6dSdd96piooKTZs2TTt27ND48eMlSYcOHfJY549//KPuvfde5ebmeoz/7Gc/U0FBgft1eHi4t+1jgGtsPq3jbR0+nXPv4ZM+nQ8A0De8DjUlJSUqKChQfn6+JKm8vFyvvPKKVq1apQULFpxT/+STT2ry5Ml66KGHJElLlixRdXW1nn76aZWXl0uS4uLiPNb53e9+p9tuu02jRo3yGA8PDz+nFpePxubTsq/YotOdXT6fO3RwkIYNDfb5vACAS8erUNPR0aG6ujoVFRW5xwIDA2W321VbW9vjOrW1tXI4HB5jOTk5Wr9+fY/1TU1NeuWVV/TCCy+c897SpUu1ZMkSXXXVVbrnnns0f/58DRrU8ya0t7ervb3d/bq1tfXzNg/93PG2Dp3u7FJpXqpGx4T5dO5hQ4OVGBXq0zkBAJeWV6Hm6NGj6urqUmxsrMd4bGysdu/e3eM6Lperx3qXy9Vj/QsvvKDw8HDdddddHuM/+tGPdNNNN2n48OHaunWrioqKdOjQIZWUlPQ4j9Pp1OLFiy920zCAjI4J0/jEyL5uAwDQz3j985O/rVq1SjNnzlRISIjH+D8e7ZkwYYKCg4P1gx/8QE6nUzab7Zx5ioqKPNZpbW1VUlKS/xoHAAB9yqtQEx0draCgIDU1NXmMNzU1nfdcl7i4uIuu/8tf/qI9e/aosrLyc3vJzMzUmTNn9MEHH2js2LHnvG+z2XoMOwAAwExeXdIdHBystLQ01dTUuMe6u7tVU1OjrKysHtfJysryqJek6urqHuufe+45paWlKSUl5XN7qa+vV2BgYI9XXAEAgMuP1z8/ORwOzZ49W+np6crIyFBpaana2trcV0PNmjVLiYmJcjqdkqQHH3xQ2dnZWrFihaZOnaq1a9fqzTff1LPPPusxb2trq9atW6cVK1ac85m1tbXatm2bbrvtNoWHh6u2tlbz58/Xd77zHQ0bNqw32w0AAAzjdajJy8vTkSNHtGjRIrlcLqWmpqqqqsp9MnBDQ4MCAz87AHTLLbeooqJCCxcu1KOPPqoxY8Zo/fr17nvUnLV27VpZlqUZM2ac85k2m01r167V448/rvb2do0cOVLz588/56oqAABw+QqwLMvq6yYuhdbWVkVGRqqlpUURERF93Q564Z3GFt258nVteGAiVz/BA/82AHN58/3Ns58AAIARCDUAAMAIhBoAAGAEQg0AADACoQYAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwAqEGAAAYgVADAACMQKgBAABGINQAAAAjEGoAAIARCDUAAMAIhBoAAGAEQg0AADACoQYAABiBUAMAAIzQq1BTVlam5ORkhYSEKDMzU9u3b79g/bp163TdddcpJCREN9xwgzZu3Ojx/ve+9z0FBAR4LJMnT/aoOXbsmGbOnKmIiAhFRUXp3nvv1cmTJ3vTPgAAMJDXoaayslIOh0PFxcXasWOHUlJSlJOTo8OHD/dYv3XrVs2YMUP33nuv3nrrLU2bNk3Tpk3TO++841E3efJkHTp0yL28+OKLHu/PnDlTO3fuVHV1tTZs2KDXXntNc+bM8bZ9AABgKK9DTUlJiQoKCpSfn6/rr79e5eXlGjJkiFatWtVj/ZNPPqnJkyfroYce0rhx47RkyRLddNNNevrppz3qbDab4uLi3MuwYcPc77377ruqqqrSf/7nfyozM1MTJ07UypUrtXbtWh08eNDbTQAAAAbyKtR0dHSorq5Odrv9swkCA2W321VbW9vjOrW1tR71kpSTk3NO/ebNmxUTE6OxY8fq3//93/Xxxx97zBEVFaX09HT3mN1uV2BgoLZt2+bNJgAAAEMN8qb46NGj6urqUmxsrMd4bGysdu/e3eM6Lperx3qXy+V+PXnyZN11110aOXKk9u3bp0cffVTf+MY3VFtbq6CgILlcLsXExHg2PmiQhg8f7jHPP2pvb1d7e7v7dWtrqzebCgAABhivQo2/TJ8+3f3nG264QRMmTNA111yjzZs3a9KkSb2a0+l0avHixb5qEQAA9HNe/fwUHR2toKAgNTU1eYw3NTUpLi6ux3Xi4uK8qpekUaNGKTo6Wnv37nXP8c8nIp85c0bHjh077zxFRUVqaWlxLwcOHPjc7QMAAAOXV0dqgoODlZaWppqaGk2bNk2S1N3drZqaGhUWFva4TlZWlmpqajRv3jz3WHV1tbKyss77OR999JE+/vhjxcfHu+dobm5WXV2d0tLSJEmvvvqquru7lZmZ2eMcNptNNpvNm82DjzQ2n9bxtg6fz7v3MJfwAwDOz+ufnxwOh2bPnq309HRlZGSotLRUbW1tys/PlyTNmjVLiYmJcjqdkqQHH3xQ2dnZWrFihaZOnaq1a9fqzTff1LPPPitJOnnypBYvXqzc3FzFxcVp3759evjhhzV69Gjl5ORIksaNG6fJkyeroKBA5eXl6uzsVGFhoaZPn66EhARf7Qv4QGPzadlXbNHpzi6/zB86OEjDhgb7ZW4AwMDmdajJy8vTkSNHtGjRIrlcLqWmpqqqqsp9MnBDQ4MCAz/7VeuWW25RRUWFFi5cqEcffVRjxozR+vXrNX78eElSUFCQ3n77bb3wwgtqbm5WQkKC7rjjDi1ZssTjSMuaNWtUWFioSZMmKTAwULm5uXrqqae+6PbDx463deh0Z5dK81I1OibM5/MPGxqsxKhQn88LABj4AizLsvq6iUuhtbVVkZGRamlpUURERF+3Y6x3Glt058rXteGBiRqfGNnX7eAywb87wFzefH/z7CcAAGAEQg0AADACoQYAABiBUAMAAIzQL+4oDAC+4K97GXHVHTAwEGoADHjDhgYrdHCQ5lXW+2X+0MFB2vTjbIIN0M8RagAMeIlRodr042y/3cl6XmW9/vr+MR338b2XOAIE+BahBoAREqNC/RIQ/HkUiCNAgG8RagDgAvx1FOjsEaDjbR2EGsBHCDUA8Dn8dRQIgG9xSTcAADACoQYAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwAqEGAAAYgVADAACMMKivG0DfaWw+reNtHT6dc+/hkz6dDwCAi0WouUw1Np+WfcUWne7s8vncoYODNGxosM/nBQDgQnoVasrKyrRs2TK5XC6lpKRo5cqVysjIOG/9unXr9Nhjj+mDDz7QmDFj9Mtf/lJTpkyRJHV2dmrhwoXauHGj9u/fr8jISNntdi1dulQJCQnuOZKTk/Xhhx96zOt0OrVgwYLebMJl73hbh053dqk0L1WjY8J8OvewocFKjAr16ZwAAHwer0NNZWWlHA6HysvLlZmZqdLSUuXk5GjPnj2KiYk5p37r1q2aMWOGnE6n7rzzTlVUVGjatGnasWOHxo8fr1OnTmnHjh167LHHlJKSouPHj+vBBx/Uv/zLv+jNN9/0mOtnP/uZCgoK3K/Dw8N7scn4R6NjwjQ+MbKv2wAA4Avz+kThkpISFRQUKD8/X9dff73Ky8s1ZMgQrVq1qsf6J598UpMnT9ZDDz2kcePGacmSJbrpppv09NNPS5IiIyNVXV2tu+++W2PHjtWXv/xlPf3006qrq1NDQ4PHXOHh4YqLi3MvQ4cO7cUmAwAAE3kVajo6OlRXVye73f7ZBIGBstvtqq2t7XGd2tpaj3pJysnJOW+9JLW0tCggIEBRUVEe40uXLtWIESN04403atmyZTpz5sx552hvb1dra6vHAgAAzOXVz09Hjx5VV1eXYmNjPcZjY2O1e/fuHtdxuVw91rtcrh7rP/nkEz3yyCOaMWOGIiIi3OM/+tGPdNNNN2n48OHaunWrioqKdOjQIZWUlPQ4j9Pp1OLFi73ZPAAAMID1q6ufOjs7dffdd8uyLD3zzDMe7zkcDvefJ0yYoODgYP3gBz+Q0+mUzWY7Z66ioiKPdVpbW5WUlOS/5gEAQJ/yKtRER0crKChITU1NHuNNTU2Ki4vrcZ24uLiLqj8baD788EO9+uqrHkdpepKZmakzZ87ogw8+0NixY89532az9Rh2AACAmbw6pyY4OFhpaWmqqalxj3V3d6umpkZZWVk9rpOVleVRL0nV1dUe9WcDzXvvvadNmzZpxIgRn9tLfX29AgMDe7ziCgAAXH68/vnJ4XBo9uzZSk9PV0ZGhkpLS9XW1qb8/HxJ0qxZs5SYmCin0ylJevDBB5Wdna0VK1Zo6tSpWrt2rd588009++yzkj4NNN/61re0Y8cObdiwQV1dXe7zbYYPH67g4GDV1tZq27Ztuu222xQeHq7a2lrNnz9f3/nOdzRs2DBf7QsAADCAeR1q8vLydOTIES1atEgul0upqamqqqpynwzc0NCgwMDPDgDdcsstqqio0MKFC/Xoo49qzJgxWr9+vcaPHy9Jamxs1O9//3tJUmpqqsdn/fnPf9bXvvY12Ww2rV27Vo8//rja29s1cuRIzZ8/3+OcGQAYiPz1aBFugonLUYBlWVZfN3EptLa2KjIyUi0tLZ97vs7l4J3GFt258nVteGAiN98D+oA/H1Uiffq4kk0/zibYYMDz5vu7X139BACXi8SoUG36cbbPHyorfXr0Z15lvY63dRBqcFkh1ABAH0mMCiV0AD7k9WMSAAAA+iNCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAI3BJNwAYyh93K+ZOxejPCDUAYJhhQ4MVOjhI8yrrfT43dypGf0aoAQDD+OtuxdypGP0doQYADMTdinE54kRhAABgBEINAAAwAqEGAAAYgVADAACMQKgBAABG4OonH2lsPu3zyyclbnQFAMDFItT4QGPzadlXbNHpzi6fz82NrgAAuDiEGh843tah051dKs1L1eiYMJ/Ny42uAAC4eIQaHxodE6bxiZF93QYAAJclQs0A4I+H0vljTgAA+hKhph/z50PppE/P1xk2NNgvcwMAcKkRavoxfz2U7iyurAIAmIRQ08/xUDoAAC4ON98DAABGINQAAAAjEGoAAIAROKcGAOAVf90SgosX8EURagAAF+VS3GaCx8L4n7+eVSj1fTDtVagpKyvTsmXL5HK5lJKSopUrVyojI+O89evWrdNjjz2mDz74QGPGjNEvf/lLTZkyxf2+ZVkqLi7Wb37zGzU3N+vWW2/VM888ozFjxrhrjh07pgceeEB/+MMfFBgYqNzcXD355JMKC/PdYwkAAOfnz9tM+POxMCZ/iXvLn88qlPo+mHodaiorK+VwOFReXq7MzEyVlpYqJydHe/bsUUxMzDn1W7du1YwZM+R0OnXnnXeqoqJC06ZN044dOzR+/HhJ0hNPPKGnnnpKL7zwgkaOHKnHHntMOTk52rVrl0JCQiRJM2fO1KFDh1RdXa3Ozk7l5+drzpw5qqio+IK7AABwsQbabSZM/xL3lr+eVSj1j+cVeh1qSkpKVFBQoPz8fElSeXm5XnnlFa1atUoLFiw4p/7JJ5/U5MmT9dBDD0mSlixZourqaj399NMqLy+XZVkqLS3VwoUL9a//+q+SpP/6r/9SbGys1q9fr+nTp+vdd99VVVWV/vrXvyo9PV2StHLlSk2ZMkXLly9XQkJCr3cAAMBcpn+J95apzyr0KtR0dHSorq5ORUVF7rHAwEDZ7XbV1tb2uE5tba0cDofHWE5OjtavXy9Jev/99+VyuWS3293vR0ZGKjMzU7W1tZo+fbpqa2sVFRXlDjSSZLfbFRgYqG3btunf/u3fzvnc9vZ2tbe3u1+3tLRIklpbW73Z5Ity8kSruttP6eSJVrW2Bvh8fgAw3dn/jr69/5BOnvDdf6f3H2lTd/spxYV266pw3/73+eSJbr/07E9n94c/vq/89V149nvbsqzPrfUq1Bw9elRdXV2KjY31GI+NjdXu3bt7XMflcvVY73K53O+fHbtQzT//tDVo0CANHz7cXfPPnE6nFi9efM54UlLS+TbvC8sq9dvUAHBZmFnqn3n9+d9nf/XsT/7cH/6a+8SJE4qMvPDRJWOvfioqKvI4QtTd3a1jx45pxIgRCgjwbTptbW1VUlKSDhw4oIiICJ/Ofblj3/oP+9a/2L/+w771r/62fy3L0okTJy7qVBOvQk10dLSCgoLU1NTkMd7U1KS4uLge14mLi7tg/dn/bWpqUnx8vEdNamqqu+bw4cMec5w5c0bHjh077+fabDbZbDaPsaioqAtv4BcUERHRL/4BmIh96z/sW/9i//oP+9a/+tP+/bwjNGd5dUfh4OBgpaWlqaamxj3W3d2tmpoaZWVl9bhOVlaWR70kVVdXu+tHjhypuLg4j5rW1lZt27bNXZOVlaXm5mbV1dW5a1599VV1d3crMzPTm00AAACG8vrnJ4fDodmzZys9PV0ZGRkqLS1VW1ub+2qoWbNmKTExUU6nU5L04IMPKjs7WytWrNDUqVO1du1avfnmm3r22WclSQEBAZo3b55+/vOfa8yYMe5LuhMSEjRt2jRJ0rhx4zR58mQVFBSovLxcnZ2dKiws1PTp07nyCQAASOpFqMnLy9ORI0e0aNEiuVwupaamqqqqyn2ib0NDgwIDPzsAdMstt6iiokILFy7Uo48+qjFjxmj9+vXue9RI0sMPP6y2tjbNmTNHzc3Nmjhxoqqqqtz3qJGkNWvWqLCwUJMmTXLffO+pp576ItvuMzabTcXFxef83IUvjn3rP+xb/2L/+g/71r8G8v4NsC7mGikAAIB+jqd0AwAAIxBqAACAEQg1AADACIQaAABgBELNRSorK1NycrJCQkKUmZmp7du3X7C+ublZc+fOVXx8vGw2m6699lpt3LjxEnU7sHi7b0tLSzV27FiFhoYqKSlJ8+fP1yeffHKJuh04XnvtNX3zm99UQkKCAgIC3M9bu5DNmzfrpptuks1m0+jRo/X888/7vc+ByNt9+9JLL+nrX/+6rrjiCkVERCgrK0v/+7//e2maHYB682/3rP/7v//ToEGD3Ddvhafe7Nv29nb99Kc/1dVXXy2bzabk5GStWrXK/832AqHmIlRWVsrhcKi4uFg7duxQSkqKcnJyzrnL8VkdHR36+te/rg8++ED/8z//oz179ug3v/mNEhMTL3Hn/Z+3+7aiokILFixQcXGx3n33XT333HOqrKzUo48+eok77//a2tqUkpKisrKyi6p///33NXXqVN12222qr6/XvHnzdN999/Hl2wNv9+1rr72mr3/969q4caPq6up022236Zvf/KbeeustP3c6MHm7f89qbm7WrFmzNGnSJD91NvD1Zt/efffdqqmp0XPPPac9e/boxRdf1NixY/3Y5Rdg4XNlZGRYc+fOdb/u6uqyEhISLKfT2WP9M888Y40aNcrq6Oi4VC0OWN7u27lz51q33367x5jD4bBuvfVWv/Y50EmyXn755QvWPPzww9aXvvQlj7G8vDwrJyfHj50NfBezb3ty/fXXW4sXL/Z9Q4bxZv/m5eVZCxcutIqLi62UlBS/9mWCi9m3f/zjH63IyEjr448/vjRNfUEcqfkcHR0dqqurk91ud48FBgbKbrertra2x3V+//vfKysrS3PnzlVsbKzGjx+vX/ziF+rq6rpUbQ8Ivdm3t9xyi+rq6tw/Ue3fv18bN27UlClTLknPJqutrfX4u5CknJyc8/5doPe6u7t14sQJDR8+vK9bMcbq1au1f/9+FRcX93UrRvn973+v9PR0PfHEE0pMTNS1116rn/zkJzp9+nRft9YjY5/S7StHjx5VV1eX+47JZ8XGxmr37t09rrN//369+uqrmjlzpjZu3Ki9e/fqhz/8oTo7O/k/3D/ozb695557dPToUU2cOFGWZenMmTO6//77+fnJB1wuV49/F62trTp9+rRCQ0P7qDPzLF++XCdPntTdd9/d160Y4b333tOCBQv0l7/8RYMG8bXmS/v379frr7+ukJAQvfzyyzp69Kh++MMf6uOPP9bq1av7ur1zcKTGD7q7uxUTE6Nnn31WaWlpysvL009/+lOVl5f3dWsD3ubNm/WLX/xCv/71r7Vjxw699NJLeuWVV7RkyZK+bg24KBUVFVq8eLF++9vfKiYmpq/bGfC6urp0zz33aPHixbr22mv7uh3jdHd3KyAgQGvWrFFGRoamTJmikpISvfDCC/3yaA2R9nNER0crKChITU1NHuNNTU2Ki4vrcZ34+HgNHjxYQUFB7rFx48bJ5XKpo6NDwcHBfu15oOjNvn3sscf03e9+V/fdd58k6YYbbnA/N+ynP/2px3PH4J24uLge/y4iIiI4SuMja9eu1X333ad169ad81MfeufEiRN688039dZbb6mwsFDSp1/ElmVp0KBB+tOf/qTbb7+9j7scuOLj45WYmKjIyEj32Lhx42RZlj766CONGTOmD7s7F98AnyM4OFhpaWmqqalxj3V3d6umpkZZWVk9rnPrrbdq79696u7udo/9/e9/V3x8PIHmH/Rm3546deqc4HI2PFo8xuwLycrK8vi7kKTq6urz/l3AOy+++KLy8/P14osvaurUqX3djjEiIiL0t7/9TfX19e7l/vvv19ixY1VfX6/MzMy+bnFAu/XWW3Xw4EGdPHnSPfb3v/9dgYGBuvLKK/uws/Po2/OUB4a1a9daNpvNev75561du3ZZc+bMsaKioiyXy2VZlmV997vftRYsWOCub2hosMLDw63CwkJrz5491oYNG6yYmBjr5z//eV9tQr/l7b4tLi62wsPDrRdffNHav3+/9ac//cm65pprrLvvvruvNqHfOnHihPXWW29Zb731liXJKikpsd566y3rww8/tCzLshYsWGB997vfddfv37/fGjJkiPXQQw9Z7777rlVWVmYFBQVZVVVVfbUJ/Za3+3bNmjXWoEGDrLKyMuvQoUPupbm5ua82oV/zdv/+M65+Oj9v9+2JEyesK6+80vrWt75l7dy509qyZYs1ZswY67777uurTbggQs1FWrlypXXVVVdZwcHBVkZGhvXGG2+438vOzrZmz57tUb9161YrMzPTstls1qhRo6z/+I//sM6cOXOJux4YvNm3nZ2d1uOPP25dc801VkhIiJWUlGT98Ic/tI4fP37pG+/n/vznP1uSzlnO7s/Zs2db2dnZ56yTmppqBQcHW6NGjbJWr159yfseCLzdt9nZ2Resh6fe/Nv9R4Sa8+vNvn333Xctu91uhYaGWldeeaXlcDisU6dOXfrmL0KAZXHMHgAADHycUwMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEf4fFLFVtxjuVg4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "HDweight = HDwt.copy()\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.01:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "c06c0980-fad8-4596-bf2f-ad46bd6bb074",
   "metadata": {},
   "outputs": [],
   "source": [
    "savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "80050c17-7680-4ea9-b803-dfc5bc057e5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping\n",
    "HDunitList = [savedHDunitList[t].copy() for t in range(nHDs) ]   #restart\n",
    "HDvPop = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.axvline(aDP, ls=\"--\")\n",
    "plt.axvline(minDistrictPop, ls=\"dotted\")\n",
    "plt.axvline(maxDistrictPop, ls=\"dotted\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "827b8496-d66d-40ae-9780-a20243a54f92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the HD centers for 50 cantFill HDs with border or big enclaves\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are the HD centers for\",len(cantFill),\"cantFill HDs with border or big enclaves\")\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u],0.1)\n",
    "for t in cantFill:\n",
    "    plotPoly(HDCP[t].buffer(0.02))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "d4eec53d-e431-4e8d-a430-31ef4d1aa8c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 800\n",
      "working on avg unit-to-HDcp dist for HD 1602\n",
      "working on avg unit-to-HDcp dist for HD 2402\n",
      "working on avg unit-to-HDcp dist for HD 3203\n",
      "working on avg unit-to-HDcp dist for HD 4004\n",
      "working on avg unit-to-HDcp dist for HD 4806\n",
      "working on avg unit-to-HDcp dist for HD 5606\n",
      "working on avg unit-to-HDcp dist for HD 6406\n",
      "working on avg unit-to-HDcp dist for HD 7206\n",
      "working on avg unit-to-HDcp dist for HD 8006\n",
      "working on avg unit-to-HDcp dist for HD 8807\n",
      "all unit-toHDcp distances computed\n"
     ]
    }
   ],
   "source": [
    "barredJettisonSet = set([]) #for basic muni snap, there are no special corner units to freeze\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "print(\"all unit-toHDcp distances computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "76225203-dcf1-4845-b6a2-3b75e3749b01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for muniSnap99 code, all units are munis, not clusters\n",
      "remember, there are 1 units that are district-sized\n"
     ]
    }
   ],
   "source": [
    "print(\"for muniSnap99 code, all units are munis, not clusters\")\n",
    "allUnits = [u for u in range(nUnits)]\n",
    "wholeSet = set()\n",
    "for u in range(nUnits):\n",
    "    if unitPop[u] > minDistrictPop and unitPop[u] <= maxDistrictPop :\n",
    "        wholeSet.add(u)\n",
    "print(\"remember, there are\",len(wholeSet),\"units that are district-sized\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "e3230001-cd19-4802-bd4d-c4590bdb0f3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "badDiscoList = cantFill.copy() #sPgenerator + eLgenerator #cantFill.copy() #sPgenerator.copy()  #prep for below\n",
    "CCBgeom = list()  #no CCBgeoms in this ipynb\n",
    "#HDpoly = HDvtdGeom.copy()  #as shortcut, did not generate these shapes for the COI conversion code\n",
    "vtdArea = [tractGeom[v].area for v in range(nVTDs) ]\n",
    "HDarea = [np.sum([vtdArea[v] for v in HDvtdList[t] ]) for t in range(nHDs) ]\n",
    "HDcircle = [HDCP[t].buffer(0.23456*HDarea[t]**0.5) for t in range(nHDs) ] #approximation\n",
    "HDdiam = [HDarea[h] for h in range(nHDs)]\n",
    "maxGap = max(maxDistrictPop - aDP, 0.9 * np.median(unitPop) ) # = 0.05 * aDP\n",
    "HDnAddedUnits = [list() for h in range(nHDs)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71d50f26-c2ec-403f-92e1-895f214f1a2c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "187137f1-1009-49a1-8b46-715cc49e0ba8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "since these are running fast and MUSTFREE is kludgy, go straight to MUSTSPAWN code - swelling\n",
      "And now, the major Disco's  Redraw fully using HDswell\n",
      "Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\n",
      "This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is 50\n",
      "swell-generating HD 475 our 1 th HD we must regrow out of 50 0 sec elapsed\n",
      "swell-generating HD 2482 our 11 th HD we must regrow out of 50 0 sec elapsed\n",
      "swell-generating HD 5312 our 21 th HD we must regrow out of 50 0 sec elapsed\n",
      "swell-generating HD 5367 our 31 th HD we must regrow out of 50 0 sec elapsed\n",
      "swell-generating HD 5411 our 41 th HD we must regrow out of 50 0 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "print(\"since these are running fast and MUSTFREE is kludgy, go straight to MUSTSPAWN code - swelling\")\n",
    "\n",
    "#THIS IS THE MUSTSPAWN code block - stolen from vanillaHD-OHredo\n",
    "print(\"And now, the major Disco's  Redraw fully using HDswell\")\n",
    "print(\"Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\")\n",
    "print(\"This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is\",len(badDiscoList))\n",
    "#avgDiam = MAP.area**0.5 / float(nDistricts)\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(badDiscoList):\n",
    "    if i%10 == 0:\n",
    "        print(\"swell-generating HD\",t,\"our\",i+1,\"th HD we must regrow out of\",len(badDiscoList),int(time.time()-startTime),\"sec elapsed\" )\n",
    "    notInCluster = True\n",
    "    for jj, geo in enumerate(CCBgeom):  #swell in-corner HDs from the corner cluster\n",
    "        if geo.contains(hdCP[t]):\n",
    "            starterU = allUnits.index(jj+0.25)\n",
    "            notInCluster = False\n",
    "    if notInCluster:\n",
    "        starterU = homeU[t]\n",
    "        #distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        #starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = [starterU]  #we used getContigFromStarter(starterU, HDvtdList[t], unitNbrs) in above code block\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigUs.copy(), list()\n",
    "    adjoiners = list( set( getAdjoiners(currList, unitNbrs) ).difference(wholeSet) )\n",
    "    nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "    #                                            subbed HDcircle for HDpoly in below\n",
    "    nearHDscore = [ (0.2*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDcircle[t]) + \n",
    "        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] for uu in adjoiners ]\n",
    "    stillGoing = True\n",
    "    \n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]: # ... and add its nonHD, nonwhole neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap and uu not in wholeSet:  \n",
    "                    nearHDlist.append(uu)                           #subbed HDcircle for HDpoly in below\n",
    "                    nearHDscore.append((0.1*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDcircle[t]) + \n",
    "                                                                        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] )\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts       \n",
    "    HDunitList[t] = contigUs + addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c7d49e2-454f-492d-9d7f-ed28598f59da",
   "metadata": {},
   "outputs": [],
   "source": [
    "#THIS IS THE MUSTFREE CODE - for high-pop HDs with map-border enclaves, can't fill; \n",
    "#   skipped; see another code for how this runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "597a6d42-0310-4544-b7ce-543a90d09dca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classify updated muni HDs by contiguity\n",
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 0\n",
      "working on HD 1402 time is now 0\n",
      "working on HD 2102 time is now 0\n",
      "working on HD 2802 time is now 0\n",
      "working on HD 3503 time is now 1\n",
      "working on HD 4204 time is now 1\n",
      "working on HD 4906 time is now 1\n",
      "working on HD 5606 time is now 2\n",
      "working on HD 6306 time is now 2\n",
      "working on HD 7006 time is now 3\n",
      "working on HD 7706 time is now 3\n",
      "working on HD 8407 time is now 4\n",
      "out of 8934 total HDs, there were 8934.0 contiguous and 8934.0 complement-contiguous HDs\n",
      "0 HDs had both discontiguity problems, while enclave-only = 0 and discontig only= 0\n"
     ]
    }
   ],
   "source": [
    "print(\"classify updated muni HDs by contiguity\") #take from vanillaHD\n",
    "\n",
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "if len(smallPieceLists) + len(enclaveLists) > 0:\n",
    "    print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "          len(smallPieceLists),len(enclaveLists) )\n",
    "    plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "             cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "    plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "             cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "    plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "    plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "d5ff711c-4bf6-437e-89fe-f30dda1a51a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's visualize over-used and underused units, < 0.75 or > 1.25\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here is the histogram of HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "minUnitUse, maxUnitUse = 0.75, 1.25\n",
    "print(\"let's visualize over-used and underused units, <\",minUnitUse,\"or >\",maxUnitUse)\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minUnitUse:\n",
    "        plotPoly(unitGeom[u],0.4)\n",
    "        plotCenter(\"u\",unitCP[u])\n",
    "    if unitUse[u] > maxUnitUse:\n",
    "        plotPoly(unitGeom[u], 1.5)\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c],0.1)\n",
    "plt.show()\n",
    "print(\"and here is the histogram of HD pops\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins = [0, 0.3*aDP, 0.45*aDP,0.6*aDP, 0.7*aDP, 0.85*aDP, 0.9*aDP, 0.95*aDP,\n",
    "         0.98*aDP, aDP, 1.02*aDP, 1.05*aDP, 1.1*aDP, 1.15*aDP, 1.3*aDP, 1.5*aDP, 2.0*aDP])\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "c2428bb3-f225-4e1f-aacb-abba4cc2cffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before rectifying pops, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 0.98054 0.12868\n"
     ]
    }
   ],
   "source": [
    "print(\"before rectifying pops, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        latestHDpop[t] += unitPop[u]\n",
    "        latestUnitUse[u] += nDistricts * HDweight[t]\n",
    "latestUnitWeights, latestUnitDistro = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "raw",
   "id": "c4ca3fe7-43de-48cb-b06c-b52ddb0f9fb6",
   "metadata": {},
   "source": [
    "HDunitList = [latestHDlist[t].copy() for t in range(nHDs)] #restart\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts*HDweight[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "6b3dd3ff-706e-4e0d-a705-3a82cd62b519",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now square up the 1555 HDs with pop < 113227 to within 5959\n",
      "working on squaring up HD 5 time is now 0\n",
      "working on squaring up HD 1263 time is now 0\n",
      "working on squaring up HD 1546 time is now 0\n",
      "working on squaring up HD 2627 time is now 0\n",
      "working on squaring up HD 3247 time is now 1\n",
      "working on squaring up HD 3822 time is now 1\n",
      "working on squaring up HD 4146 time is now 2\n",
      "working on squaring up HD 4571 time is now 2\n",
      "working on squaring up HD 5091 time is now 3\n",
      "working on squaring up HD 6060 time is now 3\n",
      "working on squaring up HD 6801 time is now 4\n",
      "working on squaring up HD 7095 time is now 4\n",
      "working on squaring up HD 7302 time is now 5\n",
      "working on squaring up HD 7527 time is now 5\n",
      "working on squaring up HD 8249 time is now 6\n",
      "working on squaring up HD 8797 time is now 6\n",
      "We have attempted to address underpop in a total of 1555 HDs\n",
      "... but we got stuck in 351\n",
      "Here is a scatterplot of original (x) to final pop (y)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING UP UNDERPOPPED\n",
    "HDnAddedUnits, HDaddedPop = [0]*nHDs, [0.]*nHDs\n",
    "underPoppedList, stillUnderPoppedList = list(),list()\n",
    "minDistrictPop, maxDistrictPop = 0.95 * aDP, 1.05 * aDP\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop < minDistrictPop and t in popHDlist:\n",
    "        underPoppedList.append(t)\n",
    "maxGap = maxDistrictPop - aDP \n",
    "print(\"We will now square up the\",len(underPoppedList),\"HDs with pop <\",int(minDistrictPop),\"to within\",int(maxGap) )\n",
    "startTime = time.time()\n",
    "for ii,t in enumerate(underPoppedList):\n",
    "    if ii%100 == 0:\n",
    "        print(\"working on squaring up HD\",t,\"time is now\",int(time.time() - startTime)) \n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "    if HDvPop[t] < minDistrictPop:\n",
    "        stillUnderPoppedList.append(t)\n",
    "print(\"We have attempted to address underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "if len(stillUnderPoppedList) > 0:\n",
    "    print(\"... but we got stuck in\",len(stillUnderPoppedList))\n",
    "print(\"Here is a scatterplot of original (x) to final pop (y)\")\n",
    "plt.scatter([HDvPop[t] - HDaddedPop[t] for t in underPoppedList], [HDvPop[t] for t in underPoppedList])\n",
    "plt.axhline(y=aDP, xmin = 0.9*aDP, xmax = 1.1*aDP, ls=\"--\")\n",
    "plt.axvline(x=aDP, ymin = 0.9*aDP, ymax = 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "72398364-3e4a-4a03-a2cf-58a21bdea412",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous unit avg and sd usage are 0.98054 0.12868\n",
      "amped unit avg and sd usage are 0.99756 0.14177\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prevUnitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        prevUnitUse[u] += HDweight[t] * nDistricts\n",
    "\n",
    "ampedUnitUse = [0. for u in range(nUnits)]\n",
    "ampedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "ampedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in ampedHDlist[t]:\n",
    "        ampedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "plt.hist(prevUnitUse,bins=50,weights=unitPop,label=\"previous\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.legend()\n",
    "ampedUnitUseAvg, ampedUnitUseSD = getWeightedAvgAndSD(ampedUnitUse,unitPop)\n",
    "print(\"previous unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "f703cd30-fc17-4e4e-a458-40d9c74b266b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\n",
      "Let's now square down the 1043 overPopped HDs to within 5959\n",
      "working on squaring down HD 0 time is now 0\n",
      "can't drop any more units to HD 1168 without creating an enclave\n",
      "can't drop any more units to HD 1207 without creating an enclave\n",
      "can't drop any more units to HD 1281 without creating an enclave\n",
      "can't drop any more units to HD 1520 without creating an enclave\n",
      "can't drop any more units to HD 1564 without creating an enclave\n",
      "working on squaring down HD 1909 time is now 0\n",
      "can't drop any more units to HD 2134 without creating an enclave\n",
      "can't drop any more units to HD 3739 without creating an enclave\n",
      "working on squaring down HD 3926 time is now 0\n",
      "can't drop any more units to HD 4353 without creating an enclave\n",
      "can't drop any more units to HD 4388 without creating an enclave\n",
      "can't drop any more units to HD 4521 without creating an enclave\n",
      "can't drop any more units to HD 4525 without creating an enclave\n",
      "can't drop any more units to HD 4647 without creating an enclave\n",
      "can't drop any more units to HD 4951 without creating an enclave\n",
      "can't drop any more units to HD 5051 without creating an enclave\n",
      "working on squaring down HD 5093 time is now 0\n",
      "working on squaring down HD 6718 time is now 0\n",
      "working on squaring down HD 8706 time is now 1\n",
      "We have attempted to address overpop in a total of 1043 HDs\n",
      "... but we got stuck in 305\n",
      "Here is a scatterplot of original to final pop\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAGdCAYAAAD+JxxnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAABS+klEQVR4nO3de1xUdf4/8NcgzADaDCDCMAWKaSpmmBpIq/azZhmUNVG/39Rc14w0N2wzXTU3b7XtFy+1ZUaa/SrbSzf3l+QtjfCCJqKipKiRFoomA6vIjCD3+fz+8DtnGa4zONzmvJ6PxzweO+e855zPOfH1vL7nfD6foxBCCBARERHJkFt7N4CIiIiovTAIERERkWwxCBEREZFsMQgRERGRbDEIERERkWwxCBEREZFsMQgRERGRbDEIERERkWy5t3cDOjKLxYKrV6/irrvugkKhaO/mEBERkR2EELh58yZ0Oh3c3Jq+58Mg1ISrV68iODi4vZtBRERELXD58mXcc889TdYwCDXhrrvuAnD7RKrV6nZuDREREdnDbDYjODhYuo43hUGoCdbHYWq1mkGIiIiok7GnWws7SxMREZFsMQgRERGRbDEIERERkWwxCBEREZFsMQgRERGRbDEIERERkWwxCBEREZFsMQgRERGRbHFCRSIiImpzp/NMePzdQxAAFAC2PTcCg0I0bd4OBiEiIiJqU71e2mnzXQAY9+4hAMDFVbFt2hY+GiMiIqI2UzcEObre2RiEiIiIqE2czjM5tc4ZGISIiIioTTz+v4+/nFXnDAxCRERE1CaEk+ucgUGIiIiI2oTCyXXOwCBEREREbWLbcyOcWucMDEJERETUJuydJ6gt5xNiECIiIqI209w8QW09jxAnVCQiIqI2dXFVLGeWJiIiIvkaFKJBbhvf/WkIH40RERGRbDEIERERkWwxCBEREZFsMQgRERGRbDEIERERkWwxCBEREZFsMQgRERGRbDEIERERkWw5HITS0tIwbtw46HQ6KBQKJCcn26wvKCjAU089BZ1OB29vb8TExOD8+fM2NeXl5UhISED37t3RrVs3TJo0CQUFBTY1eXl5iI2Nhbe3NwICArBw4UJUV1fb1Ozfvx9DhgyBSqVCnz59sHnz5nrtTUpKQq9eveDp6YnIyEgcPXrU0UMmIiIiF+VwECotLUV4eDiSkpLqrRNCIC4uDj///DO++uornDx5Ej179oRer0dpaalU9+KLL2L79u3YsmULDhw4gKtXr2LixInS+pqaGsTGxqKyshKHDx/Gxx9/jM2bN2P58uVSTW5uLmJjYzF69GhkZWVh3rx5eOaZZ7Bnzx6p5vPPP8f8+fOxYsUKnDhxAuHh4TAYDCgsLHT0sImIiMgViTsAQGzdulX6npOTIwCI7OxsaVlNTY3o0aOHeP/994UQQhQXFwsPDw+xZcsWqebcuXMCgEhPTxdCCLFr1y7h5uYmjEajVLNhwwahVqtFRUWFEEKIRYsWiYEDB9q0Z/LkycJgMEjfIyIiREJCgk1bdDqdSExMtOv4TCaTACBMJpNd9URERNT+HLl+O7WPUEVFBQDA09NTWubm5gaVSoVDhw4BADIzM1FVVQW9Xi/V9O/fHyEhIUhPTwcApKenY9CgQQgMDJRqDAYDzGYzzpw5I9XU3oa1xrqNyspKZGZm2tS4ublBr9dLNQ2132w223yIiIjIdTk1CFkDzZIlS3Djxg1UVlZi9erVuHLlCvLz8wEARqMRSqUSPj4+Nr8NDAyE0WiUamqHIOt667qmasxmM8rKynDt2jXU1NQ0WGPdRl2JiYnQaDTSJzg4uGUngoiIiDoFpwYhDw8PfPnll/jxxx/h5+cHb29v7Nu3D2PGjIGbW8cfoLZkyRKYTCbpc/ny5fZuEhEREbUid2dvcOjQocjKyoLJZEJlZSV69OiByMhIDBs2DACg1WpRWVmJ4uJim7tCBQUF0Gq1Uk3d0V3WUWW1a+qONCsoKIBarYaXlxe6dOmCLl26NFhj3UZdKpUKKpWq5QdPREREnUqr3abRaDTo0aMHzp8/j+PHj2P8+PEAbgclDw8PpKamSrU5OTnIy8tDVFQUACAqKgqnT5+2Gd2VkpICtVqNsLAwqab2Nqw11m0olUoMHTrUpsZisSA1NVWqISIiInlz+I5QSUkJLly4IH3Pzc1FVlYW/Pz8EBISgi1btqBHjx4ICQnB6dOn8cILLyAuLg7R0dEAbgek+Ph4zJ8/H35+flCr1Xj++ecRFRWF4cOHAwCio6MRFhaG6dOnY82aNTAajVi6dCkSEhKkOzZz5szBO++8g0WLFuHpp5/G3r178cUXX2Dnzp1S2+bPn48ZM2Zg2LBhiIiIwFtvvYXS0lLMnDnzjk4aERERuQhHh6Tt27dPAKj3mTFjhhBCiHXr1ol77rlHeHh4iJCQELF06VJpyLtVWVmZeO6554Svr6/w9vYWEyZMEPn5+TY1Fy9eFGPGjBFeXl7C399fLFiwQFRVVdVry+DBg4VSqRS9e/cWH330Ub32rl+/XoSEhAilUikiIiLEkSNH7D5WDp8nIiLqfBy5fiuEEKIdc1iHZjabodFoYDKZoFar27s5REREZAdHrt8dfygXERERUSthECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2XI4CKWlpWHcuHHQ6XRQKBRITk62WV9SUoK5c+finnvugZeXF8LCwrBx40abmvLyciQkJKB79+7o1q0bJk2ahIKCApuavLw8xMbGwtvbGwEBAVi4cCGqq6ttavbv348hQ4ZApVKhT58+2Lx5c732JiUloVevXvD09ERkZCSOHj3q6CETERGRi3I4CJWWliI8PBxJSUkNrp8/fz52796Nf/zjHzh37hzmzZuHuXPnYtu2bVLNiy++iO3bt2PLli04cOAArl69iokTJ0rra2pqEBsbi8rKShw+fBgff/wxNm/ejOXLl0s1ubm5iI2NxejRo5GVlYV58+bhmWeewZ49e6Sazz//HPPnz8eKFStw4sQJhIeHw2AwoLCw0NHDJiIiIlck7gAAsXXrVptlAwcOFK+++qrNsiFDhoiXX35ZCCFEcXGx8PDwEFu2bJHWnzt3TgAQ6enpQgghdu3aJdzc3ITRaJRqNmzYINRqtaioqBBCCLFo0SIxcOBAm/1MnjxZGAwG6XtERIRISEiQvtfU1AidTicSExPtOj6TySQACJPJZFc9ERERtT9Hrt9O7yP08MMPY9u2bfjll18ghMC+ffvw448/Ijo6GgCQmZmJqqoq6PV66Tf9+/dHSEgI0tPTAQDp6ekYNGgQAgMDpRqDwQCz2YwzZ85INbW3Ya2xbqOyshKZmZk2NW5ubtDr9VJNXRUVFTCbzTYfIiIicl1OD0Lr169HWFgY7rnnHiiVSsTExCApKQmjRo0CABiNRiiVSvj4+Nj8LjAwEEajUaqpHYKs663rmqoxm80oKyvDtWvXUFNT02CNdRt1JSYmQqPRSJ/g4OCWnQQiIiLqFFolCB05cgTbtm1DZmYm3njjDSQkJODbb7919q6cbsmSJTCZTNLn8uXL7d0kIiIiakXuztxYWVkZ/vSnP2Hr1q2IjY0FADzwwAPIysrC66+/Dr1eD61Wi8rKShQXF9vcFSooKIBWqwUAaLXaeqO7rKPKatfUHWlWUFAAtVoNLy8vdOnSBV26dGmwxrqNulQqFVQqVctPABEREXUqTr0jVFVVhaqqKri52W62S5cusFgsAIChQ4fCw8MDqamp0vqcnBzk5eUhKioKABAVFYXTp0/bjO5KSUmBWq1GWFiYVFN7G9Ya6zaUSiWGDh1qU2OxWJCamirVEBERkbw5fEeopKQEFy5ckL7n5uYiKysLfn5+CAkJwSOPPIKFCxfCy8sLPXv2xIEDB/C3v/0Nf/3rXwEAGo0G8fHxmD9/Pvz8/KBWq/H8888jKioKw4cPBwBER0cjLCwM06dPx5o1a2A0GrF06VIkJCRId2zmzJmDd955B4sWLcLTTz+NvXv34osvvsDOnTults2fPx8zZszAsGHDEBERgbfeegulpaWYOXPmHZ00IiIichGODknbt2+fAFDvM2PGDCGEEPn5+eKpp54SOp1OeHp6in79+ok33nhDWCwWaRtlZWXiueeeE76+vsLb21tMmDBB5Ofn2+zn4sWLYsyYMcLLy0v4+/uLBQsWiKqqqnptGTx4sFAqlaJ3797io48+qtfe9evXi5CQEKFUKkVERIQ4cuSI3cfK4fNERESdjyPXb4UQQrRjDuvQzGYzNBoNTCYT1Gp1ezeHiIiI7ODI9ZvvGiMiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2XJv7wYQEVH7MhaX4zfr02Aur4ba0x07nh8FrY9nezeLqE0wCBERydiAZV+jrMoifb9WWoXhq1Lh5eGGc38e044tI2obDj8aS0tLw7hx46DT6aBQKJCcnGyzXqFQNPhZu3atVFNUVIRp06ZBrVbDx8cH8fHxKCkpsdnOqVOnMHLkSHh6eiI4OBhr1qyp15YtW7agf//+8PT0xKBBg7Br1y6b9UIILF++HEFBQfDy8oJer8f58+cdPWQiIpdUNwTVVlZlwYBlX7dxi4jansNBqLS0FOHh4UhKSmpwfX5+vs3nww8/hEKhwKRJk6SaadOm4cyZM0hJScGOHTuQlpaG2bNnS+vNZjOio6PRs2dPZGZmYu3atVi5ciU2bdok1Rw+fBhTp05FfHw8Tp48ibi4OMTFxSE7O1uqWbNmDd5++21s3LgRGRkZ6Nq1KwwGA8rLyx09bCIil2IsLm80BFmVVVlgLOa/l+TaFEII0eIfKxTYunUr4uLiGq2Ji4vDzZs3kZqaCgA4d+4cwsLCcOzYMQwbNgwAsHv3bowdOxZXrlyBTqfDhg0b8PLLL8NoNEKpVAIAXnrpJSQnJ+OHH34AAEyePBmlpaXYsWOHtK/hw4dj8ODB2LhxI4QQ0Ol0WLBgAf74xz8CAEwmEwIDA7F582ZMmTKl2eMzm83QaDQwmUxQq9UtOkdERB3RsD9/g2ulVc3W+Xf1wPFl0W3QIiLnceT63aqjxgoKCrBz507Ex8dLy9LT0+Hj4yOFIADQ6/Vwc3NDRkaGVDNq1CgpBAGAwWBATk4Obty4IdXo9Xqb/RkMBqSnpwMAcnNzYTQabWo0Gg0iIyOlmroqKipgNpttPkRErshcXu3UOqLOqlWD0Mcff4y77roLEydOlJYZjUYEBATY1Lm7u8PPzw9Go1GqCQwMtKmxfm+upvb62r9rqKauxMREaDQa6RMcHOzQ8RIRdRZqT/vGythbR9RZtWoQ+vDDDzFt2jR4enaOYZhLliyByWSSPpcvX27vJhERtYodz49yah1RZ9VqUf/gwYPIycnB559/brNcq9WisLDQZll1dTWKioqg1WqlmoKCApsa6/fmamqvty4LCgqyqRk8eHCDbVapVFCpVI4cJhFRp6T18YSXh1uTHaa9PNw4nxC5vFa7I/TBBx9g6NChCA8Pt1keFRWF4uJiZGZmSsv27t0Li8WCyMhIqSYtLQ1VVf/pyJeSkoJ+/frB19dXqrF2wK5dExUVBQAIDQ2FVqu1qTGbzcjIyJBqiIjk7Nyfx8DLo+HLAOcRItkQDrp586Y4efKkOHnypAAg/vrXv4qTJ0+KS5cuSTUmk0l4e3uLDRs2NLiNmJgY8eCDD4qMjAxx6NAh0bdvXzF16lRpfXFxsQgMDBTTp08X2dnZ4rPPPhPe3t7ivffek2q+++474e7uLl5//XVx7tw5sWLFCuHh4SFOnz4t1axatUr4+PiIr776Spw6dUqMHz9ehIaGirKyMruO1WQyCQDCZDI5epqIiDqN/BtlYuire0TfP+0UQ1/dI/Jv2PdvJFFH5cj12+EgtG/fPgGg3mfGjBlSzXvvvSe8vLxEcXFxg9u4fv26mDp1qujWrZtQq9Vi5syZ4ubNmzY133//vRgxYoRQqVTi7rvvFqtWraq3nS+++ELcd999QqlUioEDB4qdO3farLdYLGLZsmUiMDBQqFQq8dhjj4mcnBy7j5VBiIiIqPNx5Pp9R/MIuTrOI0RERNT5dJh5hIiIiIg6MgYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhIiIiki0GISIiIpIth4NQWloaxo0bB51OB4VCgeTk5Ho1586dw+OPPw6NRoOuXbvioYceQl5enrS+vLwcCQkJ6N69O7p164ZJkyahoKDAZht5eXmIjY2Ft7c3AgICsHDhQlRXV9vU7N+/H0OGDIFKpUKfPn2wefPmem1JSkpCr1694OnpicjISBw9etTRQyYiIiIX5XAQKi0tRXh4OJKSkhpc/9NPP2HEiBHo378/9u/fj1OnTmHZsmXw9PSUal588UVs374dW7ZswYEDB3D16lVMnDhRWl9TU4PY2FhUVlbi8OHD+Pjjj7F582YsX75cqsnNzUVsbCxGjx6NrKwszJs3D8888wz27Nkj1Xz++eeYP38+VqxYgRMnTiA8PBwGgwGFhYWOHjYRERG5InEHAIitW7faLJs8ebL47W9/2+hviouLhYeHh9iyZYu07Ny5cwKASE9PF0IIsWvXLuHm5iaMRqNUs2HDBqFWq0VFRYUQQohFixaJgQMH1tu3wWCQvkdERIiEhATpe01NjdDpdCIxMdGu4zOZTAKAMJlMdtUTERFR+3Pk+u3UPkIWiwU7d+7EfffdB4PBgICAAERGRto8PsvMzERVVRX0er20rH///ggJCUF6ejoAID09HYMGDUJgYKBUYzAYYDabcebMGamm9jasNdZtVFZWIjMz06bGzc0Ner1eqqmroqICZrPZ5kNERESuy6lBqLCwECUlJVi1ahViYmLwzTffYMKECZg4cSIOHDgAADAajVAqlfDx8bH5bWBgIIxGo1RTOwRZ11vXNVVjNptRVlaGa9euoaampsEa6zbqSkxMhEajkT7BwcEtOxFERETUKTj9jhAAjB8/Hi+++CIGDx6Ml156Cb/5zW+wceNGZ+6qVSxZsgQmk0n6XL58ub2bRERERK3IqUHI398f7u7uCAsLs1k+YMAAadSYVqtFZWUliouLbWoKCgqg1WqlmrqjyKzfm6tRq9Xw8vKCv78/unTp0mCNdRt1qVQqqNVqmw8RERG5LqcGIaVSiYceegg5OTk2y3/88Uf07NkTADB06FB4eHggNTVVWp+Tk4O8vDxERUUBAKKionD69Gmb0V0pKSlQq9VSyIqKirLZhrXGug2lUomhQ4fa1FgsFqSmpko1REREJG/ujv6gpKQEFy5ckL7n5uYiKysLfn5+CAkJwcKFCzF58mSMGjUKo0ePxu7du7F9+3bs378fAKDRaBAfH4/58+fDz88ParUazz//PKKiojB8+HAAQHR0NMLCwjB9+nSsWbMGRqMRS5cuRUJCAlQqFQBgzpw5eOedd7Bo0SI8/fTT2Lt3L7744gvs3LlTatv8+fMxY8YMDBs2DBEREXjrrbdQWlqKmTNn3sk5IyIiIlfh6JC0ffv2CQD1PjNmzJBqPvjgA9GnTx/h6ekpwsPDRXJyss02ysrKxHPPPSd8fX2Ft7e3mDBhgsjPz7epuXjxohgzZozw8vIS/v7+YsGCBaKqqqpeWwYPHiyUSqXo3bu3+Oijj+q1d/369SIkJEQolUoREREhjhw5Yvexcvg8ERFR5+PI9VshhBDtmMM6NLPZDI1GA5PJxP5CREREnYQj12++a4yIiIhki0GIiIiIZItBiIiIiGSLQYiIiIhki0GIiIiIZItBiIiIiGSLQYiIiIhki0GIiIiIZItBiIiIiGSLQYiIiIhki0GIiIiIZItBiIiIiGSLQYiIiIhki0GIiIiIZItBiIiIiGSLQYiIiIhki0GIiIiIZItBiIiIiGSLQYiIiIhki0GIiIiIZItBiIiIiGSLQYiIiIhki0GIiIiIZItBiIiIiGSLQYiIiIhki0GIiIiIZItBiIiIiGSLQYiIiIhki0GIiIiIZItBiIiIiGTL4SCUlpaGcePGQafTQaFQIDk52Wb9U089BYVCYfOJiYmxqSkqKsK0adOgVqvh4+OD+Ph4lJSU2NScOnUKI0eOhKenJ4KDg7FmzZp6bdmyZQv69+8PT09PDBo0CLt27bJZL4TA8uXLERQUBC8vL+j1epw/f97RQyYiIiIX5XAQKi0tRXh4OJKSkhqtiYmJQX5+vvT59NNPbdZPmzYNZ86cQUpKCnbs2IG0tDTMnj1bWm82mxEdHY2ePXsiMzMTa9euxcqVK7Fp0yap5vDhw5g6dSri4+Nx8uRJxMXFIS4uDtnZ2VLNmjVr8Pbbb2Pjxo3IyMhA165dYTAYUF5e7uhhExERkSsSdwCA2Lp1q82yGTNmiPHjxzf6m7NnzwoA4tixY9Kyr7/+WigUCvHLL78IIYR49913ha+vr6ioqJBqFi9eLPr16yd9f+KJJ0RsbKzNtiMjI8Wzzz4rhBDCYrEIrVYr1q5dK60vLi4WKpVKfPrpp3Ydn8lkEgCEyWSyq56IiIjanyPX71bpI7R//34EBASgX79++P3vf4/r169L69LT0+Hj44Nhw4ZJy/R6Pdzc3JCRkSHVjBo1CkqlUqoxGAzIycnBjRs3pBq9Xm+zX4PBgPT0dABAbm4ujEajTY1Go0FkZKRUU1dFRQXMZrPNh4iIiFyX04NQTEwM/va3vyE1NRWrV6/GgQMHMGbMGNTU1AAAjEYjAgICbH7j7u4OPz8/GI1GqSYwMNCmxvq9uZra62v/rqGauhITE6HRaKRPcHCww8dPREREnYe7szc4ZcoU6X8PGjQIDzzwAO69917s378fjz32mLN351RLlizB/Pnzpe9ms5lhiIiIyIW1+vD53r17w9/fHxcuXAAAaLVaFBYW2tRUV1ejqKgIWq1WqikoKLCpsX5vrqb2+tq/a6imLpVKBbVabfMhIiIi19XqQejKlSu4fv06goKCAABRUVEoLi5GZmamVLN3715YLBZERkZKNWlpaaiqqpJqUlJS0K9fP/j6+ko1qampNvtKSUlBVFQUACA0NBRardamxmw2IyMjQ6ohIiIieXM4CJWUlCArKwtZWVkAbndKzsrKQl5eHkpKSrBw4UIcOXIEFy9eRGpqKsaPH48+ffrAYDAAAAYMGICYmBjMmjULR48exXfffYe5c+diypQp0Ol0AIAnn3wSSqUS8fHxOHPmDD7//HOsW7fO5rHVCy+8gN27d+ONN97ADz/8gJUrV+L48eOYO3cuAEChUGDevHl47bXXsG3bNpw+fRq/+93voNPpEBcXd4enjYiIiFyCo0PS9u3bJwDU+8yYMUPcunVLREdHix49eggPDw/Rs2dPMWvWLGE0Gm22cf36dTF16lTRrVs3oVarxcyZM8XNmzdtar7//nsxYsQIoVKpxN133y1WrVpVry1ffPGFuO+++4RSqRQDBw4UO3futFlvsVjEsmXLRGBgoFCpVOKxxx4TOTk5dh8rh88TERF1Po5cvxVCCNGOOaxDM5vN0Gg0MJlM7C9ERETUSThy/ea7xoiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2GISIiIhIthiEiIiISLYYhIiIiEi2HA5CaWlpGDduHHQ6HRQKBZKTkxutnTNnDhQKBd566y2b5UVFRZg2bRrUajV8fHwQHx+PkpISm5pTp05h5MiR8PT0RHBwMNasWVNv+1u2bEH//v3h6emJQYMGYdeuXTbrhRBYvnw5goKC4OXlBb1ej/Pnzzt6yEREROSiHA5CpaWlCA8PR1JSUpN1W7duxZEjR6DT6eqtmzZtGs6cOYOUlBTs2LEDaWlpmD17trTebDYjOjoaPXv2RGZmJtauXYuVK1di06ZNUs3hw4cxdepUxMfH4+TJk4iLi0NcXByys7OlmjVr1uDtt9/Gxo0bkZGRga5du8JgMKC8vNzRwyYiIiJXJO4AALF169Z6y69cuSLuvvtukZ2dLXr27CnefPNNad3Zs2cFAHHs2DFp2ddffy0UCoX45ZdfhBBCvPvuu8LX11dUVFRINYsXLxb9+vWTvj/xxBMiNjbWZr+RkZHi2WefFUIIYbFYhFarFWvXrpXWFxcXC5VKJT799FO7js9kMgkAwmQy2VVPHVOhqVz8KvFbMWDp1+JXid+KQlN5ezeJiIhakSPXb6f3EbJYLJg+fToWLlyIgQMH1lufnp4OHx8fDBs2TFqm1+vh5uaGjIwMqWbUqFFQKpVSjcFgQE5ODm7cuCHV6PV6m20bDAakp6cDAHJzc2E0Gm1qNBoNIiMjpZq6KioqYDabbT7UuT2wcg8e+p9vcaW4HLeqanCluBwP/c+3eGDlnvZuGhERdQBOD0KrV6+Gu7s7/vCHPzS43mg0IiAgwGaZu7s7/Pz8YDQapZrAwECbGuv35mpqr6/9u4Zq6kpMTIRGo5E+wcHBzR4vdVwPrNwDc3l1g+vM5dUMQ0RE5NwglJmZiXXr1mHz5s1QKBTO3HSbWLJkCUwmk/S5fPlyezeJWujf5opGQ5CVubwa/zZXtFGLiIioI3JqEDp48CAKCwsREhICd3d3uLu749KlS1iwYAF69eoFANBqtSgsLLT5XXV1NYqKiqDVaqWagoICmxrr9+Zqaq+v/buGaupSqVRQq9U2H+qcJrx7yKl1RETkmpwahKZPn45Tp04hKytL+uh0OixcuBB79tx+DBEVFYXi4mJkZmZKv9u7dy8sFgsiIyOlmrS0NFRVVUk1KSkp6NevH3x9faWa1NRUm/2npKQgKioKABAaGgqtVmtTYzabkZGRIdWQ6yoqrWq+yIE6IiJyTe6O/qCkpAQXLlyQvufm5iIrKwt+fn4ICQlB9+7dbeo9PDyg1WrRr18/AMCAAQMQExODWbNmYePGjaiqqsLcuXMxZcoUaaj9k08+iVdeeQXx8fFYvHgxsrOzsW7dOrz55pvSdl944QU88sgjeOONNxAbG4vPPvsMx48fl4bYKxQKzJs3D6+99hr69u2L0NBQLFu2DDqdDnFxcQ6fKOpc/Lp64FZxjV11REQkXw7fETp+/DgefPBBPPjggwCA+fPn48EHH8Ty5cvt3sY///lP9O/fH4899hjGjh2LESNG2MwRpNFo8M033yA3NxdDhw7FggULsHz5cpu5hh5++GF88skn2LRpE8LDw/Gvf/0LycnJuP/++6WaRYsW4fnnn8fs2bPx0EMPoaSkBLt374anp6ejh02dzNbnRji1joiIXJNCCCHauxEdldlshkajgclkYn+hTqipUWMAoPZ0x6mVhjZsERERtQVHrt981xi5rFMrDVB7Nvz0lyGIiIiAFvQRIupMTq004N/mCkx49xCKSqvg19UDW58bgR5qVXs3jYiIOgAGIXJ5PdQqHHrpsfZuBhERdUB8NEZERESyxSBEREREssUgRERERLLFIERERESyxSBEREREssUgRERERLLFIERERESyxSBEREREssUgRERERLLFIERERESyxSBEREREssUgRERERLLFIERERESyxSBEREREssUgRERERLLFIERERESy5d7eDaDOpcYicDS3CIU3yxFwlyciQv3QxU3R7O8qqy34e/pFXCq6hZ5+3pge1QtKd7dmawA0+zsiIqKWUgghRHs3oqMym83QaDQwmUxQq9Xt3Zx2VWMReGfvBXz0XS6Ky6qk5UEaT6wYF4aY+4Ma/W3irrN4/2AuLLX+0twUwKyRoVgyNqzRGgWAun+cdX9HRERUlyPXb94Rombtzs7HS1+eRvGtqnrrjKZy/P4fJ7Dht0MaDEOJu87ivbTcesstAjbLG6ppKKHX/h3DEBER3Sk+Y6Am7c7Ox5x/nGgwBAH/CSuvbD+LGottdKmstuD9g/UDTm3vH8zFpgZCUHPeP5iLymqLw78jIiKqjUGIGlVjEXhl+9lm6wSAfFM5juYW2Sz/e/pFWJp58GoRDd/5aY5F3N4+ERHRnWAQokYdzS1Cvqnc7vrCm7a1l4puObtJbbp9IiJyfQxC1Ki6waY5AXd52nzv6eftzObU09rbJyIi18cgRNh/qgC9Xtopfd7be7u/T91g05Qgze2h9LVNj+qF5kbWuylujw5zlJsC0vB6IiKilmIQkrleL+3EU58ct1mW+E0u7v3TLtworUSQxrPZoKIAsGJcWL35hJTubpg1MrTJ384aGYrZo5quaex3nE+IiIjuFIfPy1ivl3Y2uf65T07g2VGh2JSW2+CcPgDg6+2BxImDGp1HyDrEvbl5hBqq4TxCRETU2jihYhNceULF/acK6t0JaogbgHeefBB/3nnOpuO0j5cHZv6qF+Y+2pczSxMRUYfiyPXb4StKWloaxo0bB51OB4VCgeTkZJv1K1euRP/+/dG1a1f4+vpCr9cjIyPDpqaoqAjTpk2DWq2Gj48P4uPjUVJSYlNz6tQpjBw5Ep6enggODsaaNWvqtWXLli3o378/PD09MWjQIOzatctmvRACy5cvR1BQELy8vKDX63H+/HlHD9mlmG5VYdK739kVggDAAsC3qwqHFj+KT2cNx7opg/HprOHIXPZrvKC/z64QBNx+TBY/sjdeHX8/4kf2bjDMNFRjz++IiIhayuGrSmlpKcLDw5GUlNTg+vvuuw/vvPMOTp8+jUOHDqFXr16Ijo7Gv//9b6lm2rRpOHPmDFJSUrBjxw6kpaVh9uzZ0nqz2Yzo6Gj07NkTmZmZWLt2LVauXIlNmzZJNYcPH8bUqVMRHx+PkydPIi4uDnFxccjOzpZq1qxZg7fffhsbN25ERkYGunbtCoPBgPJyx0ZDuYpH1u5F+KvfIDOv2KHfFd4sRxc3BaLu7Y7xg+9G1L3d7Q5AREREHdkdPRpTKBTYunUr4uLiGq2x3p769ttv8dhjj+HcuXMICwvDsWPHMGzYMADA7t27MXbsWFy5cgU6nQ4bNmzAyy+/DKPRCKVSCQB46aWXkJycjB9++AEAMHnyZJSWlmLHjh3SvoYPH47Bgwdj48aNEEJAp9NhwYIF+OMf/wgAMJlMCAwMxObNmzFlypRmj8+VHo09snYvLl0va9FvP501HFH3dndyi4iIiFpHqz4ac0RlZSU2bdoEjUaD8PBwAEB6ejp8fHykEAQAer0ebm5u0iO09PR0jBo1SgpBAGAwGJCTk4MbN25INXq93mZ/BoMB6enpAIDc3FwYjUabGo1Gg8jISKmmroqKCpjNZptPZ1VjEfju/DW8vucH/GX7mRaHIDeg3rB4IiIiV9Eqo8Z27NiBKVOm4NatWwgKCkJKSgr8/f0BAEajEQEBAbaNcHeHn58fjEajVBMaajukOjAwUFrn6+sLo9EoLatdU3sbtX/XUE1diYmJeOWVV1pyyB3K7ux8LP7XKZjKq+94W+/+dggfgxERkctqlTtCo0ePRlZWFg4fPoyYmBg88cQTKCwsbI1dOdWSJUtgMpmkz+XLl9u7SQ6zviTVGSFoYyNvlCciInIVrRKEunbtij59+mD48OH44IMP4O7ujg8++AAAoNVq64Wi6upqFBUVQavVSjUFBQU2NdbvzdXUXl/7dw3V1KVSqaBWq20+nUmNReDFz7PueDtLokPx0/+MZQgiIiKX1yZjkS0WCyoqKgAAUVFRKC4uRmZmprR+7969sFgsiIyMlGrS0tJQVVUl1aSkpKBfv37w9fWValJTU232k5KSgqioKABAaGgotFqtTY3ZbEZGRoZU42oOX7iGsipLi3///fJoXFwVi2cfrT9LNBERkStyuI9QSUkJLly4IH3Pzc1FVlYW/Pz80L17d/zlL3/B448/jqCgIFy7dg1JSUn45Zdf8N///d8AgAEDBiAmJgazZs3Cxo0bUVVVhblz52LKlCnQ6XQAgCeffBKvvPIK4uPjsXjxYmRnZ2PdunV48803pf2+8MILeOSRR/DGG28gNjYWn332GY4fPy4NsVcoFJg3bx5ee+019O3bF6GhoVi2bBl0Ol2To9w6iwvGEox5+wCqLICHG/D1Hx7B/ztxpcXb69ndCxpvDye2kDoieya2JCKSE4eHz+/fvx+jR4+ut3zGjBnYuHEjnnzySWRkZODatWvo3r07HnroISxduhQPPfSQVFtUVIS5c+di+/btcHNzw6RJk/D222+jW7duUs2pU6eQkJCAY8eOwd/fH88//zwWL15ss88tW7Zg6dKluHjxIvr27Ys1a9Zg7Nix0nohBFasWIFNmzahuLgYI0aMwLvvvov77rvPrmPtqMPnQ1/a2eDrLlqqZ3cvHFj4qBO3SB1R4q6zdr3qhIios3Pk+s1XbDShowUhe1+L4YjHw4Pw+n8P5l0BF5e46yzeS8ttdP2zoxiGiMh1dJh5hMg5SsqrG3xLvDNs+z4f/ZZ+jcRdZ52+beoYKqsteP9g4yEIuP3C28rqlvcvIyLqrBiEOrjH3zmI+1fuadV9CADvpeUyDLmov6dftHkc1hCLuF1HRCQ3rTKhIt25b45fxex/nWzTfb6XlovuXVV46lehfFTmQi4V3XJqHRGRK2EQ6mBMt6oQ/uo3d7QNNwUQcJcnjGbHXy77P1//gFW7f7jjDrQ1FoGjuUUovFmOgLs8ERHqxyH57aSnn7dT64iIXAmDUAcyak0q8oocDy91WQSw/DdheO6TEy3+vbVjbUvC0O7sfLyy/SzyTf85liCNJ1aMC+Mkje1gelQv/GXXuSYfj7kpbtcREckNn390EEP//I1TQpDVXSp3bPztEPjcwdxALelAuzs7H7//xwmbEAQARlM5fv+PE9idnd/i9lDLKN3dMGtkaJM1s0bycSgRyRP/5Wtn3xy/il4v7cT10qrmix3w6bFLiLk/CJlLf40X9fdB4+V4IHK0A22NReCV7WcbnOPIuuyV7WdR01zPXXK6JWPD8OyoUNR9Oumm4NB5IpI3PhprJ2nZhfjdP4612vYP/PhvAEAXNwVe0PfF3Ef7YN5nJ7D9lNGh7TjSgfZoblG9O0G1CQD5pnIczS1C1L3dHWoH3bklY8OwILo/Z5YmIqqFQagd9HppZ6vvo7rOXZcubgqsf3IodD5nsSkt1+6ZqR3pQFt4075He/bWUcs01VFd6e6G+JG927mFREQdB4NQG2uLEAQAPl4eSP/pOob29EXmpRu4UlSKPWeMOPWLCQrAriDkaAfagLs8nVpHjmNHdSIixzAItaE5m/e32b4KblZi6vtH7mgb3VTuDg15v1FaCTcFGh2dpACg1dy+Q0HOZ+2oXvf0WzuqJz35IHy7qmA0laGotBJ+3VTQqjm1ARHJG4NQG6mstmD3D6Xt3QyHmMurEb7ya7z+xIPN3k3YnZ2PhE/qX4TrWjEurEUX3c40L5G1rUZzOYpKKuDXVQmtxqtV22xPR/W5n55sMKTyjhERyRmDUBvprK8vKKkUmPOPE9j42yGNXiibughbuSmAd6b+J1A5Emw60+Oehtpq1VSb7zToNddRHWj8Tl3+/94x2tDEf2MiIlfFINRGOvvrC17Zfha/DtM2eHG29yLs21UFwLFg09zjnra6eFdWW5odbdVYW60aCxzOCHrO6IDe1H9jIiJXxXGzbSTYt3O/viDfVI4jP11vcJ0jo8UcmXCxo8xLlLjrLPov+xp/3nkOf0u/hD/vPIf+y762eUmtPXfFgNvtrt1mZ01Aeacd0GtPbUBEJCcMQm2kf+Bd7d2EO5bwScMXZnsvwj/k38SftmbbHWwcmZeoMTUWgfSfruOrrF+Q/tN1h0NT4q6zeC8tt95jJetrSKxhyJ67YlbWNjsz6EWE+iFI44k7vZfDqQ2ISG4YhNpIUVllezfhjhWXVTV4l8Lei/CGAz+hqLTx81A32NzpvES7s/MxYvVeTH3/CF74LAtT3z+CEav32n2XpbLagvcP5jZZY30NiaMBIuWs0SlBz6qLmwIrxt2eHfpOwhCnNiAiuWEQaiP+/9s/xhXUvUvhrIuwlTVU+Hez75w1dPF2xiOnv6dfbPJFpcB/XkPiaID48LuL+PasfbN82xuyYu4PwobfDoFWY9sWe7r8KHC7XxKnNiAiuWEQaiMW4Rrv12rsLoX1IhyovvM7CgF3eWJ3dj4WfJHVZF1jF29nPXLKvWbfdAe510odfjSlALA16xe7ah0JWTH3B+HQ4kfx6azhWDdlMD6dNRzvTB1iV7taOrUBEVFnxiDURtJ/brijcWfV0F2KmPuD8MZ/h7d4m9Zgc6O0Ar//xwkYzRVN1gINX7yd9cipwGzfnZgCc7nNXTF7CABFpVXw66psNKS09C5NFzcFou7tjvGD70bUvd0x9oHbITVI03CgCtJ4cug8EckWh8+3kavFZe3dBKdq7C7FtdLGw0tTrGFgWWwY/ryz+dFX2iaGlzvrnWc91PY9mrPWWe+KNTaPUEPiBuvw0XcX6732pKmg1xIx9wfh12Ha2xM9NjKzdFtOWtmZJsgkItfGINRG7vbxau8mNEjj6Y4BQXfh9FUzSitqmq1v7jUZLe1saw02Gi+lXSHi9f8Kx6/6+t9RG5qru9e/m13bsdbVWAQ0XkosMvTDibwb+PuRvGZ/++swLSJC/eqFp6aCXktZ7xQ1pC0nrexME2QSketjEGojD/fxR9L+n9q7GTamDw/Bysfvr3c34OK1W3jr2x8BOH6XwtpXxp4w003VBX+OG2RzV+IrO/vNNHXnydoGo6m8wTtL9r7zbHpUL/xl17kmO0xbX0zb0MXd3veudXFTSHdr2uMOSVtOWtlRJsgkIrJiH6E2Mrx3w/+feHtSKBTSxbZ2v5IX9H2R9OQQ+HZV2tT7dVUi6cmmL1SO9JVx7+KGx8N1iLq3u9QOZ9zNaWoUmyOPnJTubpg1MrTJmlkjQ7H3h4IGR6g1N+Ksdhvq9utpqxDUlpNWdpQJMomIamMQaiOV1Zb2bkI9Pf3+M9t1ZbUFHxz8Gcu/ysYfv8jCqzvO1pvz53ppJf6882yzQ89j7g/Ci/q+ze6/+FZVvQ7LzY2+srcDcWNDybUOdgxeMjYMz44KrTcE3U0BPDsqFItiBtg1o3Rds0eFdog7H86cy6gj7YuIyF58NNZG/qfW6xg6AusjnRqLwLzPTmDHKaNdF3N7X9DZy7+rXe2o22HZejfn9/84cccdiGt3EG7okZO9HXaXjA3Dguj+Db5rLP2n63Z3jK5t2/f5WBQzoN07CDurY3lH2xcRkb0YhNrIxesd66WrQ0J88O1ZIxb+6xRKK5vvJF2b9X1ZTb2g804ecTU2+qolHYgb6yDsaIddpbsb4kf2rre8pRdt652PxjovtxVndSzvaPsiIrIXg1Ab6dXdGwfPt3cr/uP4pWIcv3Syxb9v7kJ+px2Wm7ubcyec2WH3Ti7aHeHOh7M6lne0fRER2Yt9hNrI4pgB7d0Ep2vqQu6MDsut0YHY2R12I0L9oLVzvqG6OsKdD2d1LO9o+yIisheDUBs5/YupvZvgdM1dyJ3VYdmZnN1ht4ubAlMjQhxqQ0d7r1db/nfqiH8TRCRvDj8aS0tLw9q1a5GZmYn8/Hxs3boVcXFxAICqqiosXboUu3btws8//wyNRgO9Xo9Vq1ZBp9NJ2ygqKsLzzz+P7du3w83NDZMmTcK6devQrdt/JrA7deoUEhIScOzYMfTo0QPPP/88Fi1aZNOWLVu2YNmyZbh48SL69u2L1atXY+zYsdJ6IQRWrFiB999/H8XFxfjVr36FDRs2oG/f5kc0OVtHeAziTPZeyO19xFVZbcHf0y8i93opFAAeDPZFkI+X0+fTcfaLTgH7O4YDHffOR2s+imzPfRERNcfhIFRaWorw8HA8/fTTmDhxos26W7du4cSJE1i2bBnCw8Nx48YNvPDCC3j88cdx/PhxqW7atGnIz89HSkoKqqqqMHPmTMyePRuffPIJAMBsNiM6Ohp6vR4bN27E6dOn8fTTT8PHxwezZ88GABw+fBhTp05FYmIifvOb3+CTTz5BXFwcTpw4gfvvvx8AsGbNGrz99tv4+OOPERoaimXLlsFgMODs2bPw9GzbxxId4TGIsyhg/4X83+YKTHj30P++V8sDW58bUe93ibvO4v2DuTbz7lhnZXbmjMO7s/PxwXcX7ap15L+XI7W+XT3w2vj7O+Sdj6Zmnu7M+yIiaopCiJa/Fl2hUNjcEWrIsWPHEBERgUuXLiEkJATnzp1DWFgYjh07hmHDhgEAdu/ejbFjx+LKlSvQ6XTYsGEDXn75ZRiNRiiVtyf1e+mll5CcnIwffvgBADB58mSUlpZix44d0r6GDx+OwYMHY+PGjRBCQKfTYcGCBfjjH/8IADCZTAgMDMTmzZsxZcqUZo/PbDZDo9HAZDJBrVa39DQBuH3H476lX9/RNjoCR4LJAyv3wFxeXW+52tMdp1YaANwOQe+l5Ta5HQVwx49NaiwCI1bvtWuoe5DGE4cWP2r3HQrrthvrBNzQ9vk6CSKi1uPI9bvV+wiZTCYoFAr4+PgAANLT0+Hj4yOFIADQ6/Vwc3NDRkaGVDNq1CgpBAGAwWBATk4Obty4IdXo9XqbfRkMBqSnpwMAcnNzYTQabWo0Gg0iIyOlmroqKipgNpttPs6S8VPnf/v8stgBOLT40TsKQQBgLq/GAyv3oLLagvcPNh2CgNv9dpZ8efqOZhxurm9QbY+HBzn0mKapTsANsY5Oa25iSiIian2tGoTKy8uxePFiTJ06VUpkRqMRAQEBNnXu7u7w8/OD0WiUagIDA21qrN+bq6m9vvbvGqqpKzExERqNRvoEBwc7fMyN+X8nrjhtW+3F/y6V9F6y9J+u46usX5D+0/V6AeXf5opGQ5CVubwaG/ZfaPY1FFY3blXhnb0tn3/AkT4/277Pdzh0NdYJuCF8nQQRUcfRavMIVVVV4YknnoAQAhs2bGit3TjVkiVLMH/+fOm72Wx2Whi6UtyxJlRsiYC7PO2aiHDCu4fs2t6mtJ8d2v9H313E3Ef7tqhTrSP9eBqbI6m5mahrdwL+7sK/8c6+xl+yW3t0GvvKEBG1n1YJQtYQdOnSJezdu9fm+ZxWq0VhYaFNfXV1NYqKiqDVaqWagoICmxrr9+Zqaq+3LgsKCrKpGTx4cIPtVqlUUKlaNidMc+7x9cbxS8Wtsu224KYA9v5QgP97MLfZiQiLSqvs2qaj718rLqtqcXCwTuZn7+OxuneQ7J2J2toJmK+TICLqHJz+aMwags6fP49vv/0W3bvbXrSioqJQXFyMzMxMadnevXthsVgQGRkp1aSlpaGq6j8X1JSUFPTr1w++vr5STWpqqs22U1JSEBUVBQAIDQ2FVqu1qTGbzcjIyJBq2lJsmLbN9+lMFgG830AIAuo/6vHr6mHXNnt086j3MtPmtDQ41O7HY4/ad5CsM1HXDVFN9fXh6ySIiDoHh4NQSUkJsrKykJWVBeB2p+SsrCzk5eWhqqoK//Vf/4Xjx4/jn//8J2pqamA0GmE0GlFZeftN5gMGDEBMTAxmzZqFo0eP4rvvvsPcuXMxZcoUaa6hJ598EkqlEvHx8Thz5gw+//xzrFu3zuax1QsvvIDdu3fjjTfewA8//ICVK1fi+PHjmDt3LoDbI9rmzZuH1157Ddu2bcPp06fxu9/9DjqdrslRbq3ls+N5bb7PtlT7Uc/W50bY9Zuv5o7CrJGhDu2nseDQXL8l4Pajq3effLDJ8FV3ssOWzkRtvQPV2K462qSKRERy5fCjsePHj2P06NHSd2s4mTFjBlauXIlt27YBQL3HT/v27cP/+T//BwDwz3/+E3PnzsVjjz0mTaj49ttvS7UajQbffPMNEhISMHToUPj7+2P58uXSHEIA8PDDD+OTTz7B0qVL8ac//Ql9+/ZFcnKyNIcQACxatAilpaWYPXs2iouLMWLECOzevbvN5xACgFNXnTcCrSMrvFmOqHu7Q+3p3mSHabWnO3qoVVgy9vZdmk1pDd9tsmrqPVSOvEB17AM6vAMFnvvkRIP7AGznSHJkJuraj+ysd6B+/48TUAA2x9ZRJ1UkIpKjO5pHyNU5cx6hEav24kpxmZNa1nF9Omu4FAjsmUfIqrLagj99eQr/OvFLvXprVGhoLqHGXqDa1G+sv7MnPH2V9Qte+Cyr4YOtZd2UwRg/+O4W74eIiJzHkes33z7fRqZFBGP1Nz+2dzNaTUN3bE6tNDQ4s3SPBl5SqnR3w+tPDIY+LLBecNA2Ehyae2ylwO3HVr8O09a782Lvax7utK8PXydBRNSxMQi1kfhR97psEGrqUU8PtQqHXnrM7m05Ehxa+tjKyp7XPFj7+jQ2a3RTj+wc2Q8REbUPvn2+jSjd3RzuGNyRKP738+yoUAS18pvDrcFh/OC7EXVv90bvnrTFEPWmZo1mX5/G2dN5nYioI+AdoTb0cmxYg/PwdAZ+XZX4y4TbLwtdFDOgQzzqaash6tZZo+19ZCd37BdFRJ0JO0s3wZmdpQFg1Jq9yCvqnB2m33wiHBOG3NPezbDR3MtOrY+tHHmBanP76wgBsCNraed1IiJn6lAvXaXbTLeqOm0IAoCi0soO93ijrR9b2fvIzl6u9viopXMuERG1Jz4aayNPbz7a3k2oR6EA7L0f+Oed5/B/D+V2uMcbnfWxlSs+PrrTzutERO2BQaiNXLXzHVetLaKXLyJCuyPq3u4w3apEwicn7e6zVPedYh1FZxui3tjjo456fu3F96sRUWfEINRGgtQqu1/42Zryim7h09lRUkjY4Kaod2eiMc3NzdOeOssQ9TuZ+6ij4/vViKgzYh+hNjJ6QEB7NwEAYDRX4GhukfQ95v4gHFr8KD6dNRxvTh6M6cNDmvx97ccb5DhHHh91Nny/GhF1RgxCbeTfNyvbuwmSlLNGm+/WuykTHrwbw3rZd5Hi442WceXHR5xziYg6IwahNhKkqf9aifbyz4w8/Pb/HsGy5NMoq6yxWcfHG63L1c+vtfO6tpUn3SQichb2EWojeddvtXcTJBXVFhy6cB2HLlzH34/k4ddhAXj/dw8BcM4rJahxcji/na3zOhHJG+8ItZHsq+b2bkKjUs4WYtbfjkkTBo69X9voRRrg4407IZfHR86ec4mIqLXwjlAb0Xh5tHcTmpRythBR/5OCwpIqaZmbAqg9911Hn5uns+iscx8REbkiBqE28vSveuPQhevt3Ywm1Q5BwH8mW3z6V73w6zAtH284ER8fERF1DAxCbUTl3vmeQlrntfk624iXYzv/45qOprPMfURE5Mo639W5k7pWWtHeTWiRzjyvDRERUXMYhNpIZx0ObdUZ57UhIiJqDoNQG4kI9YNfV2V7N6PFOnuQIyIiagiDUBvp4qbAa+Pvb+9mNEjp7sbXIhARkSwxCLWhsQ8E4dlRoe3dDAwN8cHIvv6YPjwE516NwdtTBgNw7XltiIiIGqIQQjQ0dx4BMJvN0Gg0MJlMUKvVTtvurlNXsfD/nUJpRU3zxU6mAJDz2hgo64xi252dX29emyDOa0NERJ2QI9dvDp9vB2Mf0MFwfxCO/HQdf0u/iD1nC9ps37NHhdYLQQDntSEiInniHaEmtNYdobr+svMM3j940a7abip3PDHsHvw6TIsbpZX4807buzi+3h64VVmDimqLze8UuB2ClowNc2LLiYiIOh7eEepkXo4dCDeFAu+l5TZa49/NHeumDMXw3rbvbTLcX/8uDgAcvnANX564gtLKGjzUyxczHm74ThAREZGc8Y5QE9rqjpBVZbUFH333M77OzkfutVvwcFMg/B4N1k0dim6ezKxERET2cOT6zSDUhLYOQkRERHTnHLl+81kJERERyRaDEBEREckWgxARERHJlsNBKC0tDePGjYNOp4NCoUBycrLN+i+//BLR0dHo3r07FAoFsrKy6m2jvLwcCQkJ6N69O7p164ZJkyahoMB2Lp28vDzExsbC29sbAQEBWLhwIaqrq21q9u/fjyFDhkClUqFPnz7YvHlzvX0lJSWhV69e8PT0RGRkJI4ePeroIRMREZGLcjgIlZaWIjw8HElJSY2uHzFiBFavXt3oNl588UVs374dW7ZswYEDB3D16lVMnDhRWl9TU4PY2FhUVlbi8OHD+Pjjj7F582YsX75cqsnNzUVsbCxGjx6NrKwszJs3D8888wz27Nkj1Xz++eeYP38+VqxYgRMnTiA8PBwGgwGFhYWOHjYRERG5InEHAIitW7c2uC43N1cAECdPnrRZXlxcLDw8PMSWLVukZefOnRMARHp6uhBCiF27dgk3NzdhNBqlmg0bNgi1Wi0qKiqEEEIsWrRIDBw40GbbkydPFgaDQfoeEREhEhISpO81NTVCp9OJxMREu47PZDIJAMJkMtlVT0RERO3Pket3m/cRyszMRFVVFfR6vbSsf//+CAkJQXp6OgAgPT0dgwYNQmBgoFRjMBhgNptx5swZqab2Nqw11m1UVlYiMzPTpsbNzQ16vV6qqauiogJms9nmQ0RERK6rzYOQ0WiEUqmEj4+PzfLAwEAYjUappnYIsq63rmuqxmw2o6ysDNeuXUNNTU2DNdZt1JWYmAiNRiN9goODW3ycRERE1PFx1FgtS5Ysgclkkj6XL19u7yYRERFRK2rz9zZotVpUVlaiuLjY5q5QQUEBtFqtVFN3dJd1VFntmrojzQoKCqBWq+Hl5YUuXbqgS5cuDdZYt1GXSqWCSqWSvov/nXSbj8iIiIg6D+t1W9jx8ow2D0JDhw6Fh4cHUlNTMWnSJABATk4O8vLyEBUVBQCIiorCX/7yFxQWFiIgIAAAkJKSArVajbCwMKlm165dNttOSUmRtqFUKjF06FCkpqYiLi4OAGCxWJCamoq5c+fa1dabN28CAB+RERERdUI3b96ERqNpssbhIFRSUoILFy5I33Nzc5GVlQU/Pz+EhISgqKgIeXl5uHr1KoDbIQe4fQdHq9VCo9EgPj4e8+fPh5+fH9RqNZ5//nlERUVh+PDhAIDo6GiEhYVh+vTpWLNmDYxGI5YuXYqEhATpjs2cOXPwzjvvYNGiRXj66aexd+9efPHFF9i5c6fUtvnz52PGjBkYNmwYIiIi8NZbb6G0tBQzZ86061h1Oh0uX76Mu+66CwqFovkfdCJmsxnBwcG4fPmybN+jxnPAcwDwHAA8BwDPAeBa50AIgZs3b0Kn09lV7JB9+/YJAPU+M2bMEEII8dFHHzW4fsWKFdI2ysrKxHPPPSd8fX2Ft7e3mDBhgsjPz7fZz8WLF8WYMWOEl5eX8Pf3FwsWLBBVVVX12jJ48GChVCpF7969xUcffVSvvevXrxchISFCqVSKiIgIceTIEUcP2SVxagCeAyF4DoTgORCC50AIngMh5HsO+PZ5mXLkzbyuiueA5wDgOQB4DgCeA0C+54CjxoiIiEi2GIRkSqVSYcWKFTaj5OSG54DnAOA5AHgOAJ4DQL7ngI/GiIiISLZ4R4iIiIhki0GIiIiIZItBiIiIiGSLQYiIiIhki0Gog0pLS8O4ceOg0+mgUCiQnJxss37lypXo378/unbtCl9fX+j1emRkZNjUFBUVYdq0aVCr1fDx8UF8fDxKSkpsak6dOoWRI0fC09MTwcHBWLNmTb22bNmyBf3794enpycGDRpU79UmQggsX74cQUFB8PLygl6vx/nz51v9HNQ2Z84cKBQKvPXWWzbLXf0cPPXUU1AoFDafmJgYWZ0DADh37hwef/xxaDQadO3aFQ899BDy8vKk9eXl5UhISED37t3RrVs3TJo0qd57CPPy8hAbGwtvb28EBARg4cKFqK6utqnZv38/hgwZApVKhT59+mDz5s312pKUlIRevXrB09MTkZGR9d6b2BrnoO7fgPWzdu1aqcbV/w5KSkowd+5c3HPPPfDy8kJYWBg2btxoU+PqfwcFBQV46qmnoNPp4O3tjZiYmHrnvrOfg1bRfnM5UlN27dolXn75ZfHll18KAGLr1q026//5z3+KlJQU8dNPP4ns7GwRHx8v1Gq1KCwslGpiYmJEeHi4OHLkiDh48KDo06ePmDp1qrTeZDKJwMBAMW3aNJGdnS0+/fRT4eXlJd577z2p5rvvvhNdunQRa9asEWfPnhVLly4VHh4e4vTp01LNqlWrhEajEcnJyeL7778Xjz/+uAgNDRVlZWWteg6svvzySxEeHi50Op148803bda5+jmYMWOGiImJEfn5+dKnqKhIVufgwoULws/PTyxcuFCcOHFCXLhwQXz11VeioKBAqpkzZ44IDg4Wqamp4vjx42L48OHi4YcfltZXV1eL+++/X+j1enHy5Emxa9cu4e/vL5YsWSLV/Pzzz8Lb21vMnz9fnD17Vqxfv1506dJF7N69W6r57LPPhFKpFB9++KE4c+aMmDVrlvDx8bFpS2ucg9r//fPz88WHH34oFAqF+Omnn6QaV/87mDVrlrj33nvFvn37RG5urnjvvfdEly5dxFdffSXVuPLfgcViEcOHDxcjR44UR48eFT/88IOYPXu2CAkJESUlJS5zDloDg1An0FQIsLJOjf7tt98KIYQ4e/asACCOHTsm1Xz99ddCoVCIX375RQghxLvvvit8fX1FRUWFVLN48WLRr18/6fsTTzwhYmNjbfYVGRkpnn32WSHE7f/j02q1Yu3atdL64uJioVKpxKefftqyA25AY+fgypUr4u677xbZ2dmiZ8+eNkFIDudgxowZYvz48Y3+Rg7nYPLkyeK3v/1to78pLi4WHh4eYsuWLdKyc+fOCQAiPT1dCHH7AuPm5iaMRqNUs2HDBqFWq6XzsmjRIjFw4MB6+zYYDNL3iIgIkZCQIH2vqakROp1OJCYmOn6wjbDn34Px48eLRx99VPouh7+DgQMHildffdVm2ZAhQ8TLL78stcOV/w5ycnIEAJGdnW2z3x49eoj3339fCOF658BZ+GjMBVRWVmLTpk3QaDQIDw8HAKSnp8PHxwfDhg2T6vR6Pdzc3KRHaOnp6Rg1ahSUSqVUYzAYkJOTgxs3bkg1er3eZn8GgwHp6ekAbr9012g02tRoNBpERkZKNa3FYrFg+vTpWLhwIQYOHFhvvRzOAXD7FnVAQAD69euH3//+97h+/bq0ztXPgcViwc6dO3HffffBYDAgICAAkZGRNo8MMjMzUVVVZdO2/v37IyQkRGpbeno6Bg0ahMDAQJvjM5vNOHPmjFTT1DmorKxEZmamTY2bmxv0en2b/B1YFRQUYOfOnYiPj5eWufrfAQA8/PDD2LZtG3755RcIIbBv3z78+OOPiI6OBuD6fwcVFRUAAE9PT5v9qlQqHDp0CIDrn4OWYhDqxHbs2IFu3brB09MTb775JlJSUuDv7w8AMBqNCAgIsKl3d3eHn58fjEajVFP7jx2A9L25mtrra/+uoZrWsnr1ari7u+MPf/hDg+vlcA5iYmLwt7/9DampqVi9ejUOHDiAMWPGoKamRmqbK5+DwsJClJSUYNWqVYiJicE333yDCRMmYOLEiThw4IDUNqVSCR8fnybb39JzYDabUVZWhmvXrqGmpqZd/g5q+/jjj3HXXXdh4sSJ0jJX/zsAgPXr1yMsLAz33HMPlEolYmJikJSUhFGjRkltc+W/A2ugWbJkCW7cuIHKykqsXr0aV65cQX5+vtR2Vz4HLeXe3g2glhs9ejSysrJw7do1vP/++3jiiSeQkZFR7x88V5SZmYl169bhxIkTUCgU7d2cdjNlyhTpfw8aNAgPPPAA7r33Xuzfvx+PPfZYO7asbVgsFgDA+PHj8eKLLwIABg8ejMOHD2Pjxo145JFH2rN57eLDDz/EtGnTbO4MyMH69etx5MgRbNu2DT179kRaWhoSEhKg0+nq3b1wRR4eHvjyyy8RHx8PPz8/dOnSBXq9HmPGjIHgCySaxDtCnVjXrl3Rp08fDB8+HB988AHc3d3xwQcfAAC0Wi0KCwtt6qurq1FUVAStVivV1B0tYP3eXE3t9bV/11BNazh48CAKCwsREhICd3d3uLu749KlS1iwYAF69eoltc2Vz0FDevfuDX9/f1y4cEFqmyufA39/f7i7uyMsLMxm+YABA6RRY1qtFpWVlSguLm60bXdyDtRqNby8vODv748uXbq069/BwYMHkZOTg2eeecZmuav/HZSVleFPf/oT/vrXv2LcuHF44IEHMHfuXEyePBmvv/661DZX/zsYOnQosrKyUFxcjPz8fOzevRvXr19H7969pba7+jloCQYhF2KxWKTnxFFRUSguLkZmZqa0fu/evbBYLIiMjJRq0tLSUFVVJdWkpKSgX79+8PX1lWpSU1Nt9pOSkoKoqCgAQGhoKLRarU2N2WxGRkaGVNMapk+fjlOnTiErK0v66HQ6LFy4EHv27JHa7srnoCFXrlzB9evXERQUBMD1z4FSqcRDDz2EnJwcm+U//vgjevbsCeD2xcHDw8OmbTk5OcjLy5PaFhUVhdOnT9uEhZSUFKjVailkNXcOlEolhg4dalNjsViQmpraZn8HH3zwAYYOHSr1FbRy9b+DqqoqVFVVwc3N9pLWpUsX6a6hnP4ONBoNevTogfPnz+P48eMYP348AHmdA4e0d29tatjNmzfFyZMnxcmTJwUA8de//lWcPHlSXLp0SZSUlIglS5aI9PR0cfHiRXH8+HExc+ZMoVKpbEYMxMTEiAcffFBkZGSIQ4cOib59+9oMly0uLhaBgYFi+vTpIjs7W3z22WfC29u73nBZd3d38frrr4tz586JFStWNDhc1sfHR3z11Vfi1KlTYvz48U4ZLtvUOWhI3VFjrn4Obt68Kf74xz+K9PR0kZubK7799lsxZMgQ0bdvX1FeXi6LcyDE7ekTPDw8xKZNm8T58+elobwHDx6UtjFnzhwREhIi9u7dK44fPy6ioqJEVFSUtN46ZDg6OlpkZWWJ3bt3ix49ejQ4ZHjhwoXi3LlzIikpqcEhwyqVSmzevFmcPXtWzJ49W/j4+NiMwGmNcyDE7ZGj3t7eYsOGDQ1uw9X/Dh555BExcOBAsW/fPvHzzz+Ljz76SHh6eop3331X2oar/x188cUXYt++feKnn34SycnJomfPnmLixIk22+js56A1MAh1UPv27RMA6n1mzJghysrKxIQJE4ROpxNKpVIEBQWJxx9/XBw9etRmG9evXxdTp04V3bp1E2q1WsycOVPcvHnTpub7778XI0aMECqVStx9991i1apV9dryxRdfiPvuu08olUoxcOBAsXPnTpv1FotFLFu2TAQGBgqVSiUee+wxkZOT06rnoCENBSFXPge3bt0S0dHRokePHsLDw0P07NlTzJo1q94/NK58Dqw++OAD0adPH+Hp6SnCw8NFcnKyzTbKysrEc889J3x9fYW3t7eYMGGCyM/Pt6m5ePGiGDNmjPDy8hL+/v5iwYIFoqqqql5bBg8eLJRKpejdu7f46KOP6rV3/fr1IiQkRCiVShERESGOHDnSJufgvffeE15eXqK4uLjBbbj630F+fr546qmnhE6nE56enqJfv37ijTfeEBaLRdqGq/8drFu3Ttxzzz3Cw8NDhISEiKVLl9pMh+AK56A1KIRgLyoiIiKSJ/YRIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2fr/t051zFoFtW4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING DOWN\n",
    "print(\"Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\")\n",
    "HDnShedUnits = [0]*nHDs\n",
    "overPoppedList, stillOverPoppedList = list(), list()\n",
    "startTime = time.time()\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop > maxDistrictPop and t in popHDlist:\n",
    "        overPoppedList.append(t)\n",
    "\n",
    "maxGap = aDP - minDistrictPop   \n",
    "print(\"Let's now square down the\",len(overPoppedList),\"overPopped HDs to within\", int(maxGap))   \n",
    "for ii,t in enumerate(overPoppedList):\n",
    "    if ii%200 == 0:\n",
    "        print(\"working on squaring down HD\",t,\"time is now\",int(time.time()-startTime))\n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t],unitNbrs)\n",
    "    if not (unbroken and noEnclave):\n",
    "        print(\"SKIPPING shedding attempt on overpopped HD\",t,\"with pop\",HDvPop[t],\"because it was not legal to start\")\n",
    "    else:\n",
    "        excess = HDvPop[t] - aDP\n",
    "        origExcess = excess\n",
    "        HDboundaryList, HDboundaryScore = list(), list()  #these will be dynamic lists of the overused border units to jettison\n",
    "        #distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = homeU[t] #HDunitList[t][distList.index(np.min(distList))] #update from vanillaHD\n",
    "        barredSet = barredJettisonSet.union({starterU})\n",
    "        for u in set(HDunitList[t]).difference(barredSet):\n",
    "            isBoundary = False\n",
    "            for uu in unitNbrs[u]:\n",
    "                if uu not in HDunitList[t]:\n",
    "                    isBoundary = True\n",
    "                    break\n",
    "            if isBoundary and HDvPop[t] - unitPop[u] > aDP - maxGap:  #shedding this unit won't send us too far under targetpop\n",
    "                HDboundaryList.append(u)\n",
    "                HDboundaryScore.append((1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t])  #low-score = bias toward far, overused\n",
    "        currList = HDunitList[t].copy()\n",
    "        shedList, stillGoing = list(), True\n",
    "        while excess > maxGap and len(HDboundaryList) > 0 and stillGoing:   #shed the highest-scoring neighboring overused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(HDboundaryScore), 0, True\n",
    "            while i < len(HDboundaryScore) and notYetPicked and stillGoing:        \n",
    "                listNo = idx[i]   #low (large negative) score is preferred to shed\n",
    "                unitNoToShed = HDboundaryList[listNo]  # attempt to shed this unit ...\n",
    "                tryList = list(set(currList).difference( {unitNoToShed} ) )\n",
    "                unbroken, noEnclave, sPL, ePL = enclaveCheck(tryList,unitNbrs) \n",
    "                if unbroken and noEnclave:             #... if that won't eff up contiguity\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't drop any more units without creating an enclave\n",
    "                print(\"can't drop any more units to HD\",t,\"without creating an enclave\")\n",
    "            else: #add this eligible unit\n",
    "                shedList.append(unitNoToShed)\n",
    "                excess -= unitPop[unitNoToShed]        \n",
    "                del currList[currList.index(unitNoToShed) ]\n",
    "                del HDboundaryList[listNo]\n",
    "                del HDboundaryScore[listNo]\n",
    "                for u in unitNbrs[unitNoToShed]:      # ... and ID any neighboring units that will now be on boundary after we shed this unit\n",
    "                    if u in currList and u not in HDboundaryList and (unitPop[u] <= excess + maxGap and u not in barredSet): \n",
    "                        HDboundaryList.append(u)\n",
    "                        HDboundaryScore.append( (1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t] )  #low-score, bias toward far & overused       \n",
    "                for uu in HDboundaryList.copy():\n",
    "                    if unitPop[uu] > excess + maxGap:  #checking to see if the latest pop change DQ's any large units on current boundary\n",
    "                        del HDboundaryScore[HDboundaryList.index(uu)]\n",
    "                        del HDboundaryList[HDboundaryList.index(uu)]   \n",
    "        for u in shedList:\n",
    "            unitUse[u] -= HDweight[t] * nDistricts\n",
    "        HDunitList[t], HDvPop[t] = currList.copy(), np.sum( [unitPop[u] for u in currList ] )    \n",
    "        if HDvPop[t] < minDistrictPop:\n",
    "            print(\"Oops! HD\",t,\"now has undershot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after shedding\",\n",
    "                 np.sum([unitPop[u] for u in shedList]) )        \n",
    "        if HDvPop[t] > maxDistrictPop:\n",
    "            stillOverPoppedList.append(t)\n",
    "        HDnShedUnits[t] = -1 * len(shedList)\n",
    "        if homeU[t] not in HDunitList[t]:\n",
    "            print(\"WARNING: we lost the home unit\",homeU[t],\"from HD\",HD)\n",
    "            lostHomeUlist.append(t)\n",
    "            \n",
    "print(\"We have attempted to address overpop in a total of\",len(overPoppedList),\"HDs\")\n",
    "if len(stillOverPoppedList) > 0:\n",
    "    print(\"... but we got stuck in\",len(stillOverPoppedList))\n",
    "print(\"Here is a scatterplot of original to final pop\")\n",
    "plt.scatter([ampedHDpop[t] for t in overPoppedList], [HDvPop[t] for t in overPoppedList])\n",
    "plt.axhline(aDP, 0.9*aDP, 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "9c4d12b3-8596-4a5b-937e-7ef8ed71a903",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updating our unitUse, underpopped and overpopped lists\n",
      "unit use histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit use by pop\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we now have a total of 351 underpopped HDs and 305 overpopped HDs\n"
     ]
    }
   ],
   "source": [
    "print(\"updating our unitUse, underpopped and overpopped lists\")\n",
    "unitUse = [0.]*nUnits\n",
    "HDvPop = [0.]*nHDs\n",
    "stillUnder, stillOver = list(), list()\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "        HDvPop[t] += unitPop[u]\n",
    "    if HDvPop[t] < minDistrictPop:\n",
    "        stillUnder.append(t)\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        stillOver.append(t)\n",
    "print(\"unit use histogram\")\n",
    "plt.hist(unitUse, weights = unitPop)\n",
    "plt.show()\n",
    "print(\"unit use by pop\")\n",
    "plt.scatter(unitPop,unitUse)\n",
    "plt.show()\n",
    "print(\"we now have a total of\",len(stillUnder),\"underpopped HDs and\",len(stillOver),\"overpopped HDs\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "c7b7fff8-d72b-48b6-a1f3-e131d23f1adb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([unitPop[homeU[t]] for t in stillUnder],histtype =\"step\",label=\"underPopped\")\n",
    "plt.hist([unitPop[homeU[t]] for t in stillOver],histtype =\"step\",label=\"overPopped\")\n",
    "plt.xlabel(\"homeU pop\")\n",
    "plt.legend()\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "3e80ff56-7d63-4aca-b84a-bae0cdc3d835",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are centerpoints of under (small, faint) and overpopped HDs\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are centerpoints of under (small, faint) and overpopped HDs\")\n",
    "for t in stillOver:\n",
    "    plotPoly(HDCP[t].buffer(0.03),0.5)\n",
    "for t in stillUnder:\n",
    "    plotPoly(HDCP[t].buffer(0.01),1)\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c],0.1)\n",
    "#for u in range(nUnits):\n",
    "#    if unitUse[u] > 1.2 :\n",
    "#        plotPoly(unitGeom[u],0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "f65ac44d-8755-4ff0-bad3-a1e79cf65d9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lets zoom in on SW Ohio, then Akron-Cleve area\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"lets zoom in on SW Ohio, then Akron-Cleve area\")\n",
    "for u in range(nUnits):\n",
    "    if unitCPx[u] < -84 and unitCPy[u] < 39.9:\n",
    "        plotPoly(unitGeom[u], 0.1)\n",
    "        if unitPop[u] > 0.1 * aDP:\n",
    "            plotCenter(r3(unitPop[u]/aDP),unitCP[u],8)\n",
    "for t in stillUnder:\n",
    "    if  HDCPx[t] < -84 and HDCPy[t] < 39.9:\n",
    "        plotCenter(\"u\",HDCP[t],6)\n",
    "for t in stillOver:\n",
    "    if  HDCPx[t] < -84 and HDCPy[t] < 39.9:\n",
    "        plotCenter(\"o\",HDCP[t],6)\n",
    "plt.show()\n",
    "\n",
    "for u in range(nUnits):\n",
    "    if unitCP[u].distance(Point(-81.5,41.4)) < 0.7:\n",
    "        plotPoly(unitGeom[u], 0.1)\n",
    "        if unitPop[u] > 0.1 * aDP:\n",
    "            plotCenter(r3(unitPop[u]/aDP),unitCP[u],8)\n",
    "for t in stillUnder:\n",
    "    if  HDCP[t].distance(Point(-81.5,41.4)) < 0.7:\n",
    "        plotCenter(\"u\",HDCP[t],6)\n",
    "for t in stillOver:\n",
    "    if  HDCP[t].distance(Point(-81.5,41.4)) < 0.7:\n",
    "        plotCenter(\"o\",HDCP[t],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "37944660-9197-4e58-ac32-0a2bdad79616",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05000000000000006\n"
     ]
    }
   ],
   "source": [
    "print(maxGap/aDP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "703d18f8-eccb-411f-b295-e66405ca328c",
   "metadata": {},
   "outputs": [],
   "source": [
    "saved2unitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping\n",
    "#HDunitList = [saved2unitList[t].copy() for t in range(nHDs)] #restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "2cf82304-0a6e-4e9e-a277-7783e48d0f58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "these areas are highly blocky.  Look to pair two or three units where possible\n",
      "all done trying to build 2- and 3-unit districts.  nSuccess and nFail were 247 409\n"
     ]
    }
   ],
   "source": [
    "print(\"these areas are highly blocky.  Look to pair two or three units where possible\")\n",
    "blockyList, failedList = list(), list()\n",
    "for t in stillUnder + stillOver:\n",
    "    hU = homeU[t]\n",
    "    newPop, newList = unitPop[hU], [homeU]\n",
    "    isSolved = False\n",
    "    for u in unitNbrs[homeU[t]]:\n",
    "        if abs(newPop + unitPop[u] - aDP) < maxGap:\n",
    "            newPop += unitPop[u]\n",
    "            newList = [hU,u]\n",
    "            isSolved = True\n",
    "            break\n",
    "    if abs(newPop - aDP) > maxGap:\n",
    "        for u in unitNbrs[hU]:  #try 3-way\n",
    "            trialPop = newPop + unitPop[u]\n",
    "            eitherNbrs = list( set(unitNbrs[u] + unitNbrs[hU]).difference({hU,u}) )\n",
    "            eitherDist = [unitCP[uu].distance(HDCP[t]) for uu in eitherNbrs]\n",
    "            nEither = len(eitherNbrs)\n",
    "            idx = np.argsort(eitherDist)\n",
    "            for i in range(nEither):\n",
    "                iNo = idx[i]\n",
    "                if abs(trialPop + unitPop[eitherNbrs[iNo]] - aDP) < maxGap:\n",
    "                    isSolved = True\n",
    "                    newList = [hU, u, eitherNbrs[iNo] ]\n",
    "                    break\n",
    "    if isSolved:\n",
    "        unbroken, noEnclave, __, ___ = enclaveCheck(newList,unitNbrs)\n",
    "        if unbroken and noEnclave:\n",
    "            HDunitList[t] = newList.copy()\n",
    "            HDvPop[t] = np.sum([unitPop[u] for u in newList])  #don't worry about unitUse updates; will do later\n",
    "            blockyList.append(t)\n",
    "        else:\n",
    "            isSolved = False\n",
    "    if not isSolved:\n",
    "        failedList.append(t)\n",
    "print(\"all done trying to build 2- and 3-unit districts.  nSuccess and nFail were\",len(blockyList),len(failedList))\n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "470057e6-de93-43a1-a693-241f74b57c5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before resorting to splitting units, try once more with MustSpawn\n",
      "This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is 50\n",
      "swell-generating HD 253 our 1 th HD we must regrow out of 409 0 sec elapsed\n",
      "swell-generating HD 1123 our 11 th HD we must regrow out of 409 0 sec elapsed\n",
      "swell-generating HD 1230 our 21 th HD we must regrow out of 409 3 sec elapsed\n",
      "swell-generating HD 1317 our 31 th HD we must regrow out of 409 3 sec elapsed\n",
      "swell-generating HD 1442 our 41 th HD we must regrow out of 409 5 sec elapsed\n",
      "swell-generating HD 1535 our 51 th HD we must regrow out of 409 6 sec elapsed\n",
      "swell-generating HD 1608 our 61 th HD we must regrow out of 409 9 sec elapsed\n",
      "swell-generating HD 1756 our 71 th HD we must regrow out of 409 11 sec elapsed\n",
      "swell-generating HD 2178 our 81 th HD we must regrow out of 409 11 sec elapsed\n",
      "swell-generating HD 2585 our 91 th HD we must regrow out of 409 11 sec elapsed\n",
      "swell-generating HD 2936 our 101 th HD we must regrow out of 409 11 sec elapsed\n",
      "swell-generating HD 3033 our 111 th HD we must regrow out of 409 13 sec elapsed\n",
      "swell-generating HD 3148 our 121 th HD we must regrow out of 409 15 sec elapsed\n",
      "swell-generating HD 3214 our 131 th HD we must regrow out of 409 16 sec elapsed\n",
      "swell-generating HD 3821 our 141 th HD we must regrow out of 409 17 sec elapsed\n",
      "swell-generating HD 3869 our 151 th HD we must regrow out of 409 18 sec elapsed\n",
      "swell-generating HD 3919 our 161 th HD we must regrow out of 409 18 sec elapsed\n",
      "swell-generating HD 4061 our 171 th HD we must regrow out of 409 18 sec elapsed\n",
      "swell-generating HD 4225 our 181 th HD we must regrow out of 409 20 sec elapsed\n",
      "swell-generating HD 4394 our 191 th HD we must regrow out of 409 21 sec elapsed\n",
      "swell-generating HD 4809 our 201 th HD we must regrow out of 409 22 sec elapsed\n",
      "swell-generating HD 4885 our 211 th HD we must regrow out of 409 24 sec elapsed\n",
      "swell-generating HD 5153 our 221 th HD we must regrow out of 409 26 sec elapsed\n",
      "swell-generating HD 6167 our 231 th HD we must regrow out of 409 27 sec elapsed\n",
      "swell-generating HD 6236 our 241 th HD we must regrow out of 409 29 sec elapsed\n",
      "swell-generating HD 6353 our 251 th HD we must regrow out of 409 32 sec elapsed\n",
      "swell-generating HD 8457 our 261 th HD we must regrow out of 409 32 sec elapsed\n",
      "swell-generating HD 3811 our 271 th HD we must regrow out of 409 32 sec elapsed\n",
      "swell-generating HD 4476 our 281 th HD we must regrow out of 409 32 sec elapsed\n",
      "swell-generating HD 4496 our 291 th HD we must regrow out of 409 32 sec elapsed\n",
      "swell-generating HD 4653 our 301 th HD we must regrow out of 409 33 sec elapsed\n",
      "swell-generating HD 4779 our 311 th HD we must regrow out of 409 33 sec elapsed\n",
      "swell-generating HD 4796 our 321 th HD we must regrow out of 409 33 sec elapsed\n",
      "swell-generating HD 4962 our 331 th HD we must regrow out of 409 33 sec elapsed\n",
      "swell-generating HD 5058 our 341 th HD we must regrow out of 409 33 sec elapsed\n",
      "swell-generating HD 5083 our 351 th HD we must regrow out of 409 33 sec elapsed\n",
      "swell-generating HD 5232 our 361 th HD we must regrow out of 409 33 sec elapsed\n",
      "swell-generating HD 5317 our 371 th HD we must regrow out of 409 33 sec elapsed\n",
      "swell-generating HD 5370 our 381 th HD we must regrow out of 409 33 sec elapsed\n",
      "swell-generating HD 6260 our 391 th HD we must regrow out of 409 33 sec elapsed\n",
      "swell-generating HD 6772 our 401 th HD we must regrow out of 409 33 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "print(\"before resorting to splitting units, try once more with MustSpawn\")\n",
    "\n",
    "#THIS IS THE MUSTSPAWN code block - stolen from vanillaHD-OHredo\n",
    "print(\"This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is\",len(badDiscoList))\n",
    "#avgDiam = MAP.area**0.5 / float(nDistricts)\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(failedList):\n",
    "    if i%10 == 0:\n",
    "        print(\"swell-generating HD\",t,\"our\",i+1,\"th HD we must regrow out of\",len(failedList),int(time.time()-startTime),\"sec elapsed\" )\n",
    "    notInCluster = True\n",
    "    for jj, geo in enumerate(CCBgeom):  #swell in-corner HDs from the corner cluster\n",
    "        if geo.contains(hdCP[t]):\n",
    "            starterU = allUnits.index(jj+0.25)\n",
    "            notInCluster = False\n",
    "    if notInCluster:\n",
    "        starterU = homeU[t]\n",
    "        #distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        #starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = [starterU]  #we used getContigFromStarter(starterU, HDvtdList[t], unitNbrs) in above code block\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigUs.copy(), list()\n",
    "    adjoiners = list( set( getAdjoiners(currList, unitNbrs) ).difference(wholeSet) )\n",
    "    nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "    #                                            subbed HDcircle for HDpoly in below\n",
    "    nearHDscore = [ (0.2*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDcircle[t]) + \n",
    "        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] for uu in adjoiners ]\n",
    "    stillGoing = True\n",
    "    \n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]: # ... and add its nonHD, nonwhole neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap and uu not in wholeSet:  \n",
    "                    nearHDlist.append(uu)                           #subbed HDcircle for HDpoly in below\n",
    "                    nearHDscore.append((0.1*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDcircle[t]) + \n",
    "                                                                        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] )\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts       \n",
    "    HDunitList[t] = contigUs + addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "269b3356-65cd-43f0-b960-a0a97f782e93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "update classification of HDs by contiguity\n",
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 0\n",
      "working on HD 1402 time is now 0\n",
      "working on HD 2102 time is now 0\n",
      "working on HD 2802 time is now 0\n",
      "working on HD 3503 time is now 1\n",
      "working on HD 4204 time is now 1\n",
      "working on HD 4906 time is now 1\n",
      "working on HD 5606 time is now 2\n",
      "working on HD 6306 time is now 2\n",
      "working on HD 7006 time is now 3\n",
      "working on HD 7706 time is now 3\n",
      "working on HD 8407 time is now 3\n",
      "out of 8934 total HDs, there were 8934.0 contiguous and 8934.0 complement-contiguous HDs\n",
      "0 HDs had both discontiguity problems, while enclave-only = 0 and discontig only= 0\n"
     ]
    }
   ],
   "source": [
    "print(\"update classification of HDs by contiguity\") #take from vanillaHD\n",
    "\n",
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "if len(smallPieceLists) + len(enclaveLists) > 0:\n",
    "    print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "          len(smallPieceLists),len(enclaveLists) )\n",
    "    plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "             cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "    plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "             cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "    plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "    plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "ec95e007-0d57-4391-a110-177c20f5fda6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updating our unitUse, underpopped and overpopped lists\n",
      "unit use histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit use by pop\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we now have a total of 0 underpopped HDs and 63 overpopped HDs\n"
     ]
    }
   ],
   "source": [
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.axvline(minDistrictPop,ls=\"dotted\")\n",
    "plt.axvline(maxDistrictPop,ls=\"dotted\")\n",
    "plt.show()\n",
    "\n",
    "print(\"updating our unitUse, underpopped and overpopped lists\")\n",
    "unitUse = [0.]*nUnits\n",
    "HDvPop = [0.]*nHDs\n",
    "stillUnder, stillOver = list(), list()\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "        HDvPop[t] += unitPop[u]\n",
    "    if HDvPop[t] < minDistrictPop:\n",
    "        stillUnder.append(t)\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        stillOver.append(t)\n",
    "print(\"unit use histogram\")\n",
    "plt.hist(unitUse, weights = unitPop)\n",
    "plt.show()\n",
    "print(\"unit use by pop\")\n",
    "plt.scatter(unitPop,unitUse)\n",
    "plt.show()\n",
    "print(\"we now have a total of\",len(stillUnder),\"underpopped HDs and\",len(stillOver),\"overpopped HDs\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "b0b99c26-000a-41ad-ba7a-daeb437aaeec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stillover\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"stillover\")\n",
    "for t in stillOver:\n",
    "    plotPoly(HDCP[t].buffer(0.01))\n",
    "    plotPoly(countyGeom[countyNo[t]] )\n",
    "    plotCenter(countyNo[t],countyGeom[countyNo[t]] )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "89590763-c1d9-4381-b758-1f24c9bddd06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cList = [20,42]\n",
    "for c in cList:\n",
    "    for t in countyTractList[c]:\n",
    "        if t in stillOver:\n",
    "            plotPoly(HDCP[t].buffer(0.005))\n",
    "    for u in range(nUnits):\n",
    "        if countyGeom[c].contains(unitCP[u]) or ( c == 42 and countyGeom[27].contains(unitCP[u]) ):\n",
    "            plotPoly(unitGeom[u], 0.1)\n",
    "            if unitPop[u] > 0.02 * aDP:\n",
    "                plotCenter(r3(unitPop[u]/aDP),unitCP[u],8)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "7ea70cb0-0d48-4221-adca-3049f5222eac",
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "for u in range(nUnits):\n",
    "    for t in unitTractList[u]:\n",
    "        if homeU[t] != u :\n",
    "            print(t,\"has home unit\",homeU[t],\"not \",u)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "a28d6fa5-d2c6-499e-bda1-40b1035645c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "saved3list = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "0a433f9d-14a6-40b1-966c-d0e220a9b2b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for these, there are other HDs in the same unit which have solutions\n",
      "so, we will simply reapply the solution from the closest solved HD with the same home unit\n"
     ]
    }
   ],
   "source": [
    "print(\"for these, there are other HDs in the same unit which have solutions\")\n",
    "print(\"so, we will simply reapply the solution from the closest solved HD with the same home unit\")\n",
    "for t in stillOver:\n",
    "    uu = homeU[t]\n",
    "    solvedList = list(set(unitTractList[uu]).difference(set(stillOver)))\n",
    "    if len(solvedList) == 0:\n",
    "        print(\"uh oh, for HD\",t,\" no solved HDunitLists with this home unit\")\n",
    "    else:\n",
    "        solvedDist = [tractCP[t].distance(tractCP[i]) for i in solvedList]\n",
    "        tt = solvedList[solvedDist.index(np.min(solvedDist)) ]\n",
    "        if tt not in unitTractList[homeU[t]]:\n",
    "            print(\"error!  the nearby solved vtd\",tt,\"has a different home unit than problematic t\",t)\n",
    "        else:\n",
    "            HDunitList[t] = HDunitList[tt].copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "6158df92-b799-470f-9d1c-6279df02c7e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updating our unitUse, underpopped and overpopped lists\n",
      "unit use histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit use by pop\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we now have a total of 0 underpopped HDs and 0 overpopped HDs\n"
     ]
    }
   ],
   "source": [
    "print(\"updating our unitUse, underpopped and overpopped lists\")\n",
    "unitUse = [0.]*nUnits\n",
    "HDvPop = [0.]*nHDs\n",
    "stillUnder, stillOver = list(), list()\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "        HDvPop[t] += unitPop[u]\n",
    "    if HDvPop[t] < minDistrictPop:\n",
    "        stillUnder.append(t)\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        stillOver.append(t)\n",
    "print(\"unit use histogram\")\n",
    "plt.hist(unitUse, weights = unitPop)\n",
    "plt.show()\n",
    "print(\"unit use by pop\")\n",
    "plt.scatter(unitPop,unitUse)\n",
    "plt.show()\n",
    "print(\"we now have a total of\",len(stillUnder),\"underpopped HDs and\",len(stillOver),\"overpopped HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "630ef73f-d415-4142-b365-b84c6b7f9e6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick confirmation: any contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 0\n",
      "working on HD 1402 time is now 0\n",
      "working on HD 2102 time is now 0\n",
      "working on HD 2802 time is now 0\n",
      "working on HD 3503 time is now 1\n",
      "working on HD 4204 time is now 1\n",
      "working on HD 4906 time is now 2\n",
      "working on HD 5606 time is now 2\n",
      "working on HD 6306 time is now 2\n",
      "working on HD 7006 time is now 3\n",
      "working on HD 7706 time is now 3\n",
      "working on HD 8407 time is now 4\n",
      "out of 8934 total HDs, there were 8934.0 contiguous and 8934.0 complement-contiguous HDs\n",
      "0 HDs had both discontiguity problems, while enclave-only = 0 and discontig only= 0\n"
     ]
    }
   ],
   "source": [
    "print(\"quick confirmation: any contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):   \n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "a90790d8-6f94-419b-8d88-d7476194c185",
   "metadata": {},
   "outputs": [],
   "source": [
    "splitUnitNo, splitUnitFrac, hasSplitUnit = [-999]*nHDs, [0.]*nHDs, [0]*nHDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "add6b13f-f45c-498e-82f3-c8778d0dc65e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interm save in HDunitList format\n"
     ]
    }
   ],
   "source": [
    "print(\"interm save in HDunitList format\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "outDF = pd.DataFrame( {\"vtd\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"splitUnitNo\":splitUnitNo,\"splitUnitFrac\":splitUnitFrac,\n",
    "                       \"centroid x\":HDCPx, \"centroid y\":HDCPy, \"homeU\":homeU, \"homeC\":countyNo } )\n",
    "outname = STATE+str(int(nHDs))+\"_\"+str(nDistricts)+\"schoolReady2Patch.csv\" \n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "382c7962-d3dd-4fd0-b3b8-51f8b6e7eb2b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "e5bba0ce-dc4a-494e-86b3-0fbefed0e032",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For now, we will freeze the HDs with split units or block-forced prior to patching.\n",
      "Start with underpatching. This algorithm simplified vs. vanillaHD to manage blockiness.\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.07\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.07\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this would currently cover a total of 293 units\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated number of units to try boosting usage 200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 571 units out of 1982 with usage below 0.98\n",
      "maxExchangePop is currently 0.25 fraction of avgDistrictPop\n",
      "Let's further tighten the distro with exchanges up to 29796 = aDP* 0.25\n",
      "current avg and SD of unit usage are 0.99349 0.16071 . Now trying to increase up to 200 units' underusage\n",
      "all done trying2increase usage of unit 1946 final usage = 0.96384 0 sec elapsed 41 27 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99371 0.16052\n",
      "all done trying2increase usage of unit 1759 final usage = 0.99446 1 sec elapsed 43 13 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 0.99383 0.16019\n",
      "all done trying2increase usage of unit 475 final usage = 0.77778 2 sec elapsed 23 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99389 0.16009\n",
      "all done trying2increase usage of unit 1452 final usage = 0.98245 2 sec elapsed 34 0 complete, failed patches, unstarted= 8\n",
      "current avg and SD of usage are 0.99406 0.15975\n",
      "all done trying2increase usage of unit 1942 final usage = 0.97082 6 sec elapsed 41 73 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 0.99422 0.15919\n",
      "all done trying2increase usage of unit 182 final usage = 0.55839 12 sec elapsed 0 393 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99422 0.15919\n",
      "all done trying2increase usage of unit 486 final usage = 0.62912 13 sec elapsed 4 232 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 0.99424 0.15832\n",
      "all done trying2increase usage of unit 1054 final usage = 0.99035 14 sec elapsed 32 0 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 0.9943 0.15804\n",
      "all done trying2increase usage of unit 1456 final usage = 0.98597 15 sec elapsed 28 0 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99435 0.15792\n",
      "begin usage increase for unit 375 with usage 0.62313 . Sec, total UU units tried = 15 10\n",
      "all done trying2increase usage of unit 375 final usage = 0.62313 19 sec elapsed 0 269 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99435 0.15792\n",
      "all done trying2increase usage of unit 93 final usage = 0.98677 20 sec elapsed 32 25 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99447 0.15624\n",
      "all done trying2increase usage of unit 14 final usage = 0.80172 22 sec elapsed 16 116 complete, failed patches, unstarted= 122\n",
      "current avg and SD of usage are 0.99446 0.15313\n",
      "all done trying2increase usage of unit 555 final usage = 0.67476 32 sec elapsed 1 545 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 0.99447 0.15289\n",
      "all done trying2increase usage of unit 1454 final usage = 0.99216 32 sec elapsed 24 0 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 0.99457 0.15275\n",
      "all done trying2increase usage of unit 1904 final usage = 0.98176 33 sec elapsed 27 7 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99464 0.15264\n",
      "all done trying2increase usage of unit 269 final usage = 0.98501 35 sec elapsed 25 99 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 0.99476 0.15106\n",
      "all done trying2increase usage of unit 457 final usage = 0.98571 39 sec elapsed 27 70 complete, failed patches, unstarted= 118\n",
      "current avg and SD of usage are 0.99501 0.1492\n",
      "all done trying2increase usage of unit 1463 final usage = 0.98871 40 sec elapsed 22 0 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99508 0.14909\n",
      "all done trying2increase usage of unit 1965 final usage = 0.98489 42 sec elapsed 25 27 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99512 0.14889\n",
      "begin usage increase for unit 1580 with usage 0.70606 . Sec, total UU units tried = 43 20\n",
      "all done trying2increase usage of unit 1580 final usage = 0.98717 45 sec elapsed 28 14 complete, failed patches, unstarted= 56\n",
      "current avg and SD of usage are 0.99517 0.14838\n",
      "all done trying2increase usage of unit 468 final usage = 0.98103 46 sec elapsed 26 111 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 0.99526 0.1466\n",
      "all done trying2increase usage of unit 474 final usage = 0.7126 46 sec elapsed 0 146 complete, failed patches, unstarted= 7\n",
      "current avg and SD of usage are 0.99526 0.1466\n",
      "all done trying2increase usage of unit 1679 final usage = 0.98816 47 sec elapsed 19 6 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99531 0.14658\n",
      "all done trying2increase usage of unit 432 final usage = 0.76964 104 sec elapsed 4 464 complete, failed patches, unstarted= 93\n",
      "current avg and SD of usage are 0.99535 0.14578\n",
      "all done trying2increase usage of unit 433 final usage = 0.83966 121 sec elapsed 10 370 complete, failed patches, unstarted= 24\n",
      "current avg and SD of usage are 0.99537 0.14487\n",
      "all done trying2increase usage of unit 85 final usage = 0.74431 123 sec elapsed 0 308 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99537 0.14487\n",
      "all done trying2increase usage of unit 610 final usage = 0.98993 124 sec elapsed 19 26 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 0.99543 0.14348\n",
      "all done trying2increase usage of unit 1048 final usage = 0.98895 125 sec elapsed 20 0 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 0.99547 0.14311\n",
      "all done trying2increase usage of unit 565 final usage = 0.98237 126 sec elapsed 22 13 complete, failed patches, unstarted= 11\n",
      "current avg and SD of usage are 0.99553 0.1405\n",
      "begin usage increase for unit 150 with usage 0.76027 . Sec, total UU units tried = 126 30\n",
      "all done trying2increase usage of unit 150 final usage = 0.83071 129 sec elapsed 7 264 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99556 0.13986\n",
      "all done trying2increase usage of unit 549 final usage = 0.79259 138 sec elapsed 4 494 complete, failed patches, unstarted= 11\n",
      "current avg and SD of usage are 0.99557 0.13962\n",
      "all done trying2increase usage of unit 1470 final usage = 0.98891 138 sec elapsed 18 0 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99561 0.13956\n",
      "all done trying2increase usage of unit 893 final usage = 0.98114 141 sec elapsed 17 5 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99571 0.1393\n",
      "all done trying2increase usage of unit 439 final usage = 0.98686 142 sec elapsed 23 1 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 0.99573 0.13859\n",
      "all done trying2increase usage of unit 1898 final usage = 0.9824 143 sec elapsed 19 16 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99576 0.13757\n",
      "all done trying2increase usage of unit 1733 final usage = 0.99007 144 sec elapsed 18 2 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 0.99578 0.13752\n",
      "all done trying2increase usage of unit 303 final usage = 0.77933 147 sec elapsed 0 405 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99578 0.13752\n",
      "all done trying2increase usage of unit 581 final usage = 0.96119 150 sec elapsed 12 245 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99584 0.13558\n",
      "all done trying2increase usage of unit 453 final usage = 0.93679 154 sec elapsed 10 261 complete, failed patches, unstarted= 11\n",
      "current avg and SD of usage are 0.99589 0.13445\n",
      "begin usage increase for unit 290 with usage 0.78144 . Sec, total UU units tried = 155 40\n",
      "all done trying2increase usage of unit 290 final usage = 0.80866 161 sec elapsed 3 372 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 0.99591 0.13431\n",
      "all done trying2increase usage of unit 45 final usage = 0.98531 161 sec elapsed 17 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99596 0.13397\n",
      "all done trying2increase usage of unit 258 final usage = 0.81535 170 sec elapsed 3 413 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 0.99596 0.13386\n",
      "all done trying2increase usage of unit 284 final usage = 0.98013 171 sec elapsed 18 9 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99602 0.13376\n",
      "all done trying2increase usage of unit 301 final usage = 0.98984 171 sec elapsed 19 12 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99608 0.13229\n",
      "all done trying2increase usage of unit 1933 final usage = 0.98526 173 sec elapsed 22 0 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 0.99614 0.13209\n",
      "all done trying2increase usage of unit 218 final usage = 0.98055 174 sec elapsed 14 6 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99615 0.1315\n",
      "all done trying2increase usage of unit 19 final usage = 0.98124 174 sec elapsed 19 2 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99612 0.13142\n",
      "all done trying2increase usage of unit 506 final usage = 0.85378 178 sec elapsed 6 285 complete, failed patches, unstarted= 29\n",
      "current avg and SD of usage are 0.99612 0.13078\n",
      "all done trying2increase usage of unit 1919 final usage = 0.98075 179 sec elapsed 17 0 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99613 0.13077\n",
      "begin usage increase for unit 234 with usage 0.81159 . Sec, total UU units tried = 179 50\n",
      "all done trying2increase usage of unit 234 final usage = 0.9861 180 sec elapsed 15 83 complete, failed patches, unstarted= 6\n",
      "current avg and SD of usage are 0.99614 0.12808\n",
      "all done trying2increase usage of unit 1042 final usage = 0.98807 183 sec elapsed 17 44 complete, failed patches, unstarted= 23\n",
      "current avg and SD of usage are 0.99622 0.12758\n",
      "all done trying2increase usage of unit 1628 final usage = 0.98817 184 sec elapsed 19 52 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 0.99627 0.12697\n",
      "all done trying2increase usage of unit 1016 final usage = 0.98312 186 sec elapsed 16 27 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.9963 0.12693\n",
      "all done trying2increase usage of unit 629 final usage = 0.99095 186 sec elapsed 15 0 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 0.99632 0.12687\n",
      "all done trying2increase usage of unit 87 final usage = 0.98404 187 sec elapsed 11 9 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99638 0.12616\n",
      "all done trying2increase usage of unit 285 final usage = 0.98497 187 sec elapsed 16 9 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99644 0.12602\n",
      "all done trying2increase usage of unit 520 final usage = 0.98789 197 sec elapsed 10 191 complete, failed patches, unstarted= 102\n",
      "current avg and SD of usage are 0.99657 0.12445\n",
      "all done trying2increase usage of unit 152 final usage = 0.98199 198 sec elapsed 20 30 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99655 0.12392\n",
      "all done trying2increase usage of unit 989 final usage = 0.9901 199 sec elapsed 12 6 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99658 0.12392\n",
      "begin usage increase for unit 1981 with usage 0.83299 . Sec, total UU units tried = 199 60\n",
      "all done trying2increase usage of unit 1981 final usage = 0.9802 200 sec elapsed 11 3 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 0.99658 0.1239\n",
      "all done trying2increase usage of unit 228 final usage = 0.83666 209 sec elapsed 0 482 complete, failed patches, unstarted= 9\n",
      "current avg and SD of usage are 0.99658 0.1239\n",
      "all done trying2increase usage of unit 967 final usage = 0.98551 210 sec elapsed 13 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.9966 0.12389\n",
      "all done trying2increase usage of unit 1440 final usage = 0.99086 210 sec elapsed 9 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.9966 0.12383\n",
      "all done trying2increase usage of unit 359 final usage = 0.98183 211 sec elapsed 14 25 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99664 0.12334\n",
      "all done trying2increase usage of unit 376 final usage = 0.84482 243 sec elapsed 0 522 complete, failed patches, unstarted= 51\n",
      "current avg and SD of usage are 0.99664 0.12334\n",
      "all done trying2increase usage of unit 611 final usage = 0.98644 244 sec elapsed 11 15 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 0.99667 0.12284\n",
      "all done trying2increase usage of unit 304 final usage = 0.9804 245 sec elapsed 13 39 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99669 0.12143\n",
      "all done trying2increase usage of unit 264 final usage = 0.86097 247 sec elapsed 1 321 complete, failed patches, unstarted= 19\n",
      "current avg and SD of usage are 0.99669 0.12138\n",
      "all done trying2increase usage of unit 365 final usage = 0.87078 257 sec elapsed 2 393 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 0.9967 0.12126\n",
      "begin usage increase for unit 83 with usage 0.85315 . Sec, total UU units tried = 257 70\n",
      "all done trying2increase usage of unit 83 final usage = 0.98727 258 sec elapsed 18 11 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 0.99674 0.12104\n",
      "all done trying2increase usage of unit 20 final usage = 0.98943 259 sec elapsed 19 11 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99672 0.12077\n",
      "all done trying2increase usage of unit 7 final usage = 0.99339 259 sec elapsed 17 7 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99676 0.12062\n",
      "all done trying2increase usage of unit 505 final usage = 0.98723 260 sec elapsed 11 6 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99679 0.12058\n",
      "all done trying2increase usage of unit 168 final usage = 0.98268 260 sec elapsed 11 15 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99679 0.12004\n",
      "all done trying2increase usage of unit 155 final usage = 0.87935 306 sec elapsed 2 369 complete, failed patches, unstarted= 184\n",
      "current avg and SD of usage are 0.99681 0.11975\n",
      "all done trying2increase usage of unit 478 final usage = 0.8621 313 sec elapsed 0 193 complete, failed patches, unstarted= 70\n",
      "current avg and SD of usage are 0.99681 0.11975\n",
      "all done trying2increase usage of unit 255 final usage = 0.98051 313 sec elapsed 10 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99681 0.11972\n",
      "all done trying2increase usage of unit 448 final usage = 0.98896 314 sec elapsed 12 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99684 0.11922\n",
      "all done trying2increase usage of unit 428 final usage = 0.98529 314 sec elapsed 13 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99687 0.1191\n",
      "begin usage increase for unit 239 with usage 0.86749 . Sec, total UU units tried = 314 80\n",
      "all done trying2increase usage of unit 239 final usage = 0.98527 315 sec elapsed 11 3 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99688 0.11837\n",
      "all done trying2increase usage of unit 599 final usage = 0.98772 315 sec elapsed 15 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99688 0.11825\n",
      "all done trying2increase usage of unit 127 final usage = 0.99224 319 sec elapsed 10 256 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99688 0.11776\n",
      "all done trying2increase usage of unit 338 final usage = 0.99062 320 sec elapsed 13 33 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99689 0.11748\n",
      "all done trying2increase usage of unit 75 final usage = 0.99449 320 sec elapsed 12 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99691 0.11703\n",
      "all done trying2increase usage of unit 300 final usage = 0.98596 321 sec elapsed 11 182 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 0.99694 0.11634\n",
      "all done trying2increase usage of unit 884 final usage = 0.99358 322 sec elapsed 8 2 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99698 0.11621\n",
      "all done trying2increase usage of unit 587 final usage = 0.98196 323 sec elapsed 12 66 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99702 0.11609\n",
      "all done trying2increase usage of unit 268 final usage = 0.87253 328 sec elapsed 0 485 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 0.99702 0.11609\n",
      "all done trying2increase usage of unit 625 final usage = 0.98656 328 sec elapsed 10 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99703 0.11608\n",
      "begin usage increase for unit 1667 with usage 0.87492 . Sec, total UU units tried = 328 90\n",
      "all done trying2increase usage of unit 1667 final usage = 0.98964 330 sec elapsed 12 4 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99702 0.11589\n",
      "all done trying2increase usage of unit 372 final usage = 1.01759 330 sec elapsed 10 47 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99703 0.11566\n",
      "all done trying2increase usage of unit 310 final usage = 0.9817 331 sec elapsed 11 17 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99705 0.11505\n",
      "all done trying2increase usage of unit 1045 final usage = 0.98661 332 sec elapsed 10 25 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99704 0.11498\n",
      "all done trying2increase usage of unit 354 final usage = 0.98746 333 sec elapsed 11 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99707 0.11492\n",
      "all done trying2increase usage of unit 353 final usage = 0.98328 333 sec elapsed 10 26 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 0.99709 0.11486\n",
      "all done trying2increase usage of unit 1035 final usage = 0.99026 334 sec elapsed 9 3 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99713 0.11482\n",
      "all done trying2increase usage of unit 401 final usage = 0.98116 335 sec elapsed 10 50 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99719 0.11465\n",
      "all done trying2increase usage of unit 224 final usage = 0.98006 335 sec elapsed 10 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99722 0.11449\n",
      "all done trying2increase usage of unit 577 final usage = 0.88703 339 sec elapsed 0 333 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99722 0.11449\n",
      "begin usage increase for unit 605 with usage 0.88746 . Sec, total UU units tried = 339 100\n",
      "all done trying2increase usage of unit 605 final usage = 0.98004 340 sec elapsed 9 3 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99723 0.11442\n",
      "all done trying2increase usage of unit 496 final usage = 0.99394 341 sec elapsed 9 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99724 0.11367\n",
      "all done trying2increase usage of unit 557 final usage = 0.88957 378 sec elapsed 0 488 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 0.99724 0.11367\n",
      "all done trying2increase usage of unit 342 final usage = 0.98475 379 sec elapsed 9 23 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99724 0.11341\n",
      "all done trying2increase usage of unit 1876 final usage = 0.98879 379 sec elapsed 7 2 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99725 0.1134\n",
      "all done trying2increase usage of unit 46 final usage = 0.98807 380 sec elapsed 6 104 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99724 0.11309\n",
      "all done trying2increase usage of unit 13 final usage = 0.98355 381 sec elapsed 12 38 complete, failed patches, unstarted= 6\n",
      "current avg and SD of usage are 0.99724 0.11289\n",
      "all done trying2increase usage of unit 348 final usage = 0.98724 381 sec elapsed 10 23 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 0.99725 0.11261\n",
      "all done trying2increase usage of unit 328 final usage = 0.97125 389 sec elapsed 6 201 complete, failed patches, unstarted= 21\n",
      "current avg and SD of usage are 0.99728 0.11222\n",
      "all done trying2increase usage of unit 1722 final usage = 0.98647 392 sec elapsed 9 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99729 0.11207\n",
      "begin usage increase for unit 459 with usage 0.89478 . Sec, total UU units tried = 392 110\n",
      "all done trying2increase usage of unit 459 final usage = 0.98511 392 sec elapsed 8 60 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 0.9973 0.11196\n",
      "all done trying2increase usage of unit 407 final usage = 0.9862 393 sec elapsed 10 38 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99731 0.11191\n",
      "all done trying2increase usage of unit 628 final usage = 0.98799 393 sec elapsed 9 0 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 0.99731 0.11188\n",
      "all done trying2increase usage of unit 351 final usage = 0.92118 394 sec elapsed 2 233 complete, failed patches, unstarted= 28\n",
      "current avg and SD of usage are 0.99733 0.11173\n",
      "all done trying2increase usage of unit 855 final usage = 0.99129 396 sec elapsed 9 6 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99736 0.11163\n",
      "all done trying2increase usage of unit 25 final usage = 0.99047 396 sec elapsed 9 12 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99737 0.11124\n",
      "all done trying2increase usage of unit 325 final usage = 0.98018 397 sec elapsed 9 18 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 0.99738 0.11123\n",
      "all done trying2increase usage of unit 617 final usage = 0.9806 397 sec elapsed 7 12 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.9974 0.11119\n",
      "all done trying2increase usage of unit 861 final usage = 0.983 399 sec elapsed 7 2 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99741 0.11118\n",
      "all done trying2increase usage of unit 205 final usage = 0.98624 400 sec elapsed 11 10 complete, failed patches, unstarted= 8\n",
      "current avg and SD of usage are 0.99742 0.11113\n",
      "begin usage increase for unit 139 with usage 0.9052 . Sec, total UU units tried = 400 120\n",
      "all done trying2increase usage of unit 139 final usage = 0.98161 400 sec elapsed 7 11 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99741 0.11091\n",
      "all done trying2increase usage of unit 1927 final usage = 0.98119 402 sec elapsed 7 7 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99741 0.1109\n",
      "all done trying2increase usage of unit 54 final usage = 0.98025 402 sec elapsed 6 17 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99743 0.11076\n",
      "all done trying2increase usage of unit 103 final usage = 0.90784 403 sec elapsed 0 147 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 0.99743 0.11076\n",
      "all done trying2increase usage of unit 597 final usage = 0.98741 404 sec elapsed 7 4 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99746 0.1107\n",
      "all done trying2increase usage of unit 1458 final usage = 0.98244 404 sec elapsed 7 16 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 0.99748 0.11068\n",
      "all done trying2increase usage of unit 512 final usage = 0.98566 405 sec elapsed 7 37 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 0.99748 0.11058\n",
      "all done trying2increase usage of unit 1067 final usage = 0.98917 405 sec elapsed 6 4 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99749 0.11058\n",
      "all done trying2increase usage of unit 175 final usage = 0.98847 406 sec elapsed 5 22 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99751 0.11055\n",
      "all done trying2increase usage of unit 844 final usage = 0.9803 406 sec elapsed 6 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99753 0.11044\n",
      "begin usage increase for unit 1495 with usage 0.91345 . Sec, total UU units tried = 407 130\n",
      "all done trying2increase usage of unit 1495 final usage = 0.98435 407 sec elapsed 7 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99755 0.11042\n",
      "all done trying2increase usage of unit 192 final usage = 0.9828 407 sec elapsed 6 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99755 0.11035\n",
      "all done trying2increase usage of unit 166 final usage = 0.942 430 sec elapsed 3 511 complete, failed patches, unstarted= 6\n",
      "current avg and SD of usage are 0.99756 0.11031\n",
      "all done trying2increase usage of unit 82 final usage = 0.98098 430 sec elapsed 9 19 complete, failed patches, unstarted= 6\n",
      "current avg and SD of usage are 0.99759 0.11026\n",
      "all done trying2increase usage of unit 100 final usage = 0.98295 431 sec elapsed 5 20 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99759 0.11\n",
      "all done trying2increase usage of unit 360 final usage = 0.95128 435 sec elapsed 3 467 complete, failed patches, unstarted= 17\n",
      "current avg and SD of usage are 0.99759 0.10986\n",
      "all done trying2increase usage of unit 425 final usage = 0.91878 472 sec elapsed 0 416 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99759 0.10986\n",
      "all done trying2increase usage of unit 743 final usage = 0.98951 473 sec elapsed 6 2 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99759 0.10969\n",
      "all done trying2increase usage of unit 1445 final usage = 0.99814 473 sec elapsed 6 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99759 0.10966\n",
      "all done trying2increase usage of unit 1703 final usage = 0.9858 474 sec elapsed 6 6 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99761 0.10958\n",
      "begin usage increase for unit 1614 with usage 0.92097 . Sec, total UU units tried = 474 140\n",
      "all done trying2increase usage of unit 1614 final usage = 0.98303 475 sec elapsed 7 3 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99762 0.10957\n",
      "all done trying2increase usage of unit 315 final usage = 0.98911 475 sec elapsed 6 23 complete, failed patches, unstarted= 11\n",
      "current avg and SD of usage are 0.99764 0.10955\n",
      "all done trying2increase usage of unit 1724 final usage = 0.9805 476 sec elapsed 7 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99766 0.1095\n",
      "all done trying2increase usage of unit 390 final usage = 0.9839 477 sec elapsed 4 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99766 0.10943\n",
      "all done trying2increase usage of unit 606 final usage = 0.98239 478 sec elapsed 4 25 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99765 0.10938\n",
      "all done trying2increase usage of unit 442 final usage = 0.98659 478 sec elapsed 4 3 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 0.99765 0.10931\n",
      "all done trying2increase usage of unit 1005 final usage = 0.98955 479 sec elapsed 4 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99766 0.10925\n",
      "all done trying2increase usage of unit 382 final usage = 0.95951 479 sec elapsed 3 178 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99768 0.10917\n",
      "all done trying2increase usage of unit 1115 final usage = 0.98088 480 sec elapsed 3 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99772 0.10907\n",
      "all done trying2increase usage of unit 124 final usage = 0.9331 483 sec elapsed 0 294 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99772 0.10907\n",
      "begin usage increase for unit 165 with usage 0.9333 . Sec, total UU units tried = 484 150\n",
      "all done trying2increase usage of unit 165 final usage = 0.98959 487 sec elapsed 5 31 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 0.99772 0.10893\n",
      "all done trying2increase usage of unit 716 final usage = 0.986 487 sec elapsed 5 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99771 0.10888\n",
      "all done trying2increase usage of unit 406 final usage = 0.97335 522 sec elapsed 2 241 complete, failed patches, unstarted= 204\n",
      "current avg and SD of usage are 0.99774 0.10867\n",
      "all done trying2increase usage of unit 797 final usage = 0.98338 522 sec elapsed 4 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99775 0.10865\n",
      "all done trying2increase usage of unit 417 final usage = 0.93444 523 sec elapsed 0 182 complete, failed patches, unstarted= 78\n",
      "current avg and SD of usage are 0.99775 0.10865\n",
      "all done trying2increase usage of unit 1278 final usage = 0.98394 524 sec elapsed 3 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99776 0.10863\n",
      "all done trying2increase usage of unit 536 final usage = 0.98321 524 sec elapsed 6 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99776 0.10856\n",
      "all done trying2increase usage of unit 1662 final usage = 0.98703 525 sec elapsed 6 0 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99776 0.10853\n",
      "all done trying2increase usage of unit 627 final usage = 0.98529 525 sec elapsed 4 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99776 0.10853\n",
      "all done trying2increase usage of unit 550 final usage = 0.9908 527 sec elapsed 1 55 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99781 0.10829\n",
      "begin usage increase for unit 1081 with usage 0.93727 . Sec, total UU units tried = 527 160\n",
      "all done trying2increase usage of unit 1081 final usage = 0.98757 528 sec elapsed 4 2 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 0.99779 0.10826\n",
      "all done trying2increase usage of unit 559 final usage = 0.9949 528 sec elapsed 5 26 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99781 0.10819\n",
      "all done trying2increase usage of unit 1068 final usage = 0.99274 530 sec elapsed 5 15 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99781 0.10818\n",
      "all done trying2increase usage of unit 570 final usage = 0.98891 530 sec elapsed 5 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99782 0.10812\n",
      "all done trying2increase usage of unit 850 final usage = 0.98846 531 sec elapsed 5 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99783 0.10809\n",
      "all done trying2increase usage of unit 746 final usage = 0.98541 531 sec elapsed 4 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99786 0.10796\n",
      "all done trying2increase usage of unit 630 final usage = 0.99031 532 sec elapsed 4 0 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 0.99786 0.10793\n",
      "all done trying2increase usage of unit 673 final usage = 0.98455 532 sec elapsed 4 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99787 0.10792\n",
      "all done trying2increase usage of unit 703 final usage = 0.98218 533 sec elapsed 4 0 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99789 0.10787\n",
      "all done trying2increase usage of unit 749 final usage = 0.98438 533 sec elapsed 4 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99789 0.10786\n",
      "begin usage increase for unit 493 with usage 0.94112 . Sec, total UU units tried = 533 170\n",
      "all done trying2increase usage of unit 493 final usage = 0.98657 534 sec elapsed 4 4 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.9979 0.10777\n",
      "all done trying2increase usage of unit 607 final usage = 0.99018 534 sec elapsed 5 17 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99793 0.10762\n",
      "all done trying2increase usage of unit 157 final usage = 0.9801 535 sec elapsed 3 3 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99794 0.10718\n",
      "all done trying2increase usage of unit 1052 final usage = 0.98184 537 sec elapsed 3 24 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99795 0.10717\n",
      "all done trying2increase usage of unit 1690 final usage = 0.98627 537 sec elapsed 4 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99796 0.1071\n",
      "all done trying2increase usage of unit 413 final usage = 0.99203 542 sec elapsed 4 73 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99798 0.10696\n",
      "all done trying2increase usage of unit 1941 final usage = 0.98287 542 sec elapsed 4 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99798 0.10696\n",
      "all done trying2increase usage of unit 1771 final usage = 0.98863 543 sec elapsed 4 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99798 0.10695\n",
      "all done trying2increase usage of unit 911 final usage = 0.98553 550 sec elapsed 3 72 complete, failed patches, unstarted= 9\n",
      "current avg and SD of usage are 0.99801 0.10693\n",
      "all done trying2increase usage of unit 18 final usage = 0.94472 551 sec elapsed 0 90 complete, failed patches, unstarted= 41\n",
      "current avg and SD of usage are 0.99801 0.10693\n",
      "begin usage increase for unit 319 with usage 0.945 . Sec, total UU units tried = 551 180\n",
      "all done trying2increase usage of unit 319 final usage = 0.98012 552 sec elapsed 3 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99801 0.10689\n",
      "all done trying2increase usage of unit 395 final usage = 0.98842 552 sec elapsed 4 3 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99803 0.10688\n",
      "all done trying2increase usage of unit 377 final usage = 0.98491 553 sec elapsed 3 9 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99803 0.10676\n",
      "all done trying2increase usage of unit 787 final usage = 0.98003 553 sec elapsed 4 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99804 0.10672\n",
      "all done trying2increase usage of unit 62 final usage = 0.98579 554 sec elapsed 3 36 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99804 0.10645\n",
      "all done trying2increase usage of unit 470 final usage = 0.98351 555 sec elapsed 4 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99806 0.10644\n",
      "all done trying2increase usage of unit 245 final usage = 0.98697 555 sec elapsed 4 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99806 0.10641\n",
      "all done trying2increase usage of unit 980 final usage = 0.98522 556 sec elapsed 3 11 complete, failed patches, unstarted= 6\n",
      "current avg and SD of usage are 0.99806 0.10641\n",
      "all done trying2increase usage of unit 1590 final usage = 0.98365 557 sec elapsed 3 2 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 0.99807 0.1064\n",
      "all done trying2increase usage of unit 576 final usage = 0.97603 564 sec elapsed 3 356 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 0.99807 0.10629\n",
      "begin usage increase for unit 626 with usage 0.94932 . Sec, total UU units tried = 564 190\n",
      "all done trying2increase usage of unit 626 final usage = 0.99013 564 sec elapsed 4 0 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 0.99806 0.10618\n",
      "all done trying2increase usage of unit 1405 final usage = 0.98557 565 sec elapsed 2 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99807 0.10614\n",
      "all done trying2increase usage of unit 144 final usage = 0.96187 567 sec elapsed 1 163 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 0.99806 0.10613\n",
      "all done trying2increase usage of unit 61 final usage = 0.98255 567 sec elapsed 3 2 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99808 0.10609\n",
      "all done trying2increase usage of unit 1974 final usage = 0.9861 568 sec elapsed 3 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99808 0.10609\n",
      "all done trying2increase usage of unit 883 final usage = 0.98049 569 sec elapsed 2 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99808 0.10608\n",
      "all done trying2increase usage of unit 1352 final usage = 0.98429 570 sec elapsed 3 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99809 0.10607\n",
      "all done trying2increase usage of unit 164 final usage = 0.9886 571 sec elapsed 5 3 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.9981 0.10597\n",
      "all done trying2increase usage of unit 1027 final usage = 0.99201 571 sec elapsed 3 1 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 0.99812 0.10596\n",
      "all done trying2increase usage of unit 305 final usage = 0.99142 572 sec elapsed 4 4 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99813 0.10591\n",
      "begin usage increase for unit 1665 with usage 0.95237 . Sec, total UU units tried = 572 200\n",
      "all done trying2increase usage of unit 1665 final usage = 0.98118 572 sec elapsed 2 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99814 0.10591\n"
     ]
    }
   ],
   "source": [
    "print(\"For now, we will freeze the HDs with split units or block-forced prior to patching.\")\n",
    "print(\"Start with underpatching. This algorithm simplified vs. vanillaHD to manage blockiness.\")\n",
    "\n",
    "maxSD = 0.07  #0.07  #0.05   #adjust down if distro already tight, adjust up if blocky\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "#print(\"we will stop patching on individual underused units when their usage exceeds\",r5(stopMinUse))\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        maxNtries +=1\n",
    "print(\"this would currently cover a total of\",maxNtries,\"units\")\n",
    "maxNtries = int(input(\"enter updated number of units to try boosting usage\"))\n",
    "stopMinUse = 0.98 #float(input(\"enter updated stopMinUse value for ending usage boost on a unit; I reco 0.98\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "maxExchangePop = 0.25*aDP #this is widened vs. vanillaHD to overcome blockiness\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "debug1 = 10 #int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop),\"= aDP*\",r3(maxExchangePop/aDP) ) \n",
    "maxGap = aDP - minDistrictPop #0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "nonfrozenList = list()  \n",
    "for t in popHDlist:\n",
    "    if hasSplitUnit[t] != 1 and t not in failedList:  #don't mess with the small number of HDs that had major challenges\n",
    "        nonfrozenList.append(t)\n",
    "\n",
    "attemptedSmallUs, startTime = list(), time.time()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries:\n",
    "    #simplified vs. vanillaHD - always work on the lowest-used unit.\n",
    "    eligUnitUse = unitUse.copy()\n",
    "    for u in range(nUnits):\n",
    "        if u in attemptedSmallUs: #we already tried this one, so lock it out\n",
    "            eligUnitUse[u] = 999.  #preventing re-selection\n",
    "    UUU = eligUnitUse.index(np.min(eligUnitUse))\n",
    "    attemptedSmallUs.append(UUU)\n",
    "    UUUc = [UUU] + unitNbrs[UUU]\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"begin usage increase for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Sec, total UU units tried =\",\n",
    "              int(time.time()-startTime),len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists, UUUcSet = list(), list(), set(UUUc)\n",
    "    for t in nonfrozenList: #popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUcSet) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "        idx0 = np.argsort(uuuDists)\n",
    "    hasCandidates = True\n",
    "    if len(uuuDists) == 0:  #this underused unit is buried inside others; skip it\n",
    "        hasCandidates = False\n",
    "        print(\"   Couldn't find any HDs adjacent to underused unit\",UUU)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs) and hasCandidates: #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 40 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(UUUcSet)\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not (contig and complementContig): #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "            loopUUUc = [UUU]\n",
    "        if contig and complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in uuCandidates:\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    cContig = wontEnclave(addU, list(trySet), unitNbrs, borderUnits)  #4/20/24 sub this in as faster? vs below line\n",
    "                    #contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)  #4/20/24 this is unnec slow\n",
    "                    if cContig: #contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap and u != homeU[t]:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if contig and cContig:\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates + [homeU[t]]:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            currPop = np.sum([ unitPop[u] for u in trySet] )\n",
    "            if abs(currPop - aDP) <= maxGap: \n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying2increase usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"complete, failed patches, unstarted=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "6f7f2f60-aa6c-4d46-b2e3-7d2f68841e68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " patched use avg 0.99814 and SD 0.10591\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 8941\n",
      "working on HD 800 out of 8941\n",
      "working on HD 1600 out of 8941\n",
      "working on HD 2400 out of 8941\n",
      "working on HD 3200 out of 8941\n",
      "working on HD 4000 out of 8941\n",
      "working on HD 4800 out of 8941\n",
      "working on HD 5600 out of 8941\n",
      "working on HD 6400 out of 8941\n",
      "working on HD 7200 out of 8941\n",
      "working on HD 8000 out of 8941\n",
      "working on HD 8800 out of 8941\n",
      "all done checking HD and complement contiguity for all 8941 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    if hasSplitUnit[t] == 1:\n",
    "        unitUse[splitUnitNo[t]] -= (1. - splitUnitFrac[t]) * nDistricts * HDweight[t]\n",
    "#    for u in unpatchedHDlist[t]:\n",
    "#        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "#plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "#unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\" patched use avg\", r5(patchedAvg),\"and SD\",r5(patchedSD) )\n",
    "#print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.axvline(0.95*aDP, ls=\"dotted\")\n",
    "plt.axvline(1.05*aDP, ls=\"dotted\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%800 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "5faa4270-4a8f-4777-b514-6d72cd77718b",
   "metadata": {},
   "outputs": [],
   "source": [
    "underpatchedUnitList = [HDunitList[t].copy() for t in range(nHDs) ] #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "63ff3e3c-81f2-4f54-9a92-d07fdeb12cc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current avg and SD of usage are 0.99814 0.10591\n"
     ]
    }
   ],
   "source": [
    "HDunitList = [underpatchedUnitList[t].copy() for t in range(nHDs) ] #restart\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    if hasSplitUnit[t] == 1:\n",
    "        unitUse[splitUnitNo[t]] -= (1. - splitUnitFrac[t]) * nDistricts * HDweight[t]\n",
    "\n",
    "\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "6e54e08e-d07a-418f-b730-7bbc9d336a23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we are still biased toward high overuse for large units, so do another round of overpatch\n",
      "0.08 = default maxSD. Reco'ing 0.05 for blocky, 0.08 when only partially underpatched before this\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter maxSD 0.08\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use threshold for moving on to next overused unit is 1.02\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMaxUse value 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 712 units out of 1982 with usage above 1.02\n",
      "maxExchangePop is currently 0.25 fraction of avgDistrictPop\n",
      "this block reduces overuse, with exchanges up to 29796\n",
      "current avg and SD of unit usage are 0.99814 0.10591 . Now trying to reduce up to 712 units' overusage\n",
      "starting to reduce usage for unit 208 with usage 1.78881 . Total units tried = 1\n",
      "try to drop unit 208 from 0 th HD out of 134 possible.  usage down to 1.7888123241015061\n",
      "try to drop unit 208 from 90 th HD out of 134 possible.  usage down to 1.7661503317778067\n",
      "all done trying to reduce usage of unit 208 final usage = 1.73167 0 sec elapsed 5 93 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.99814 0.1052\n",
      "starting to reduce usage for unit 154 with usage 1.61822 . Total units tried = 2\n",
      "try to drop unit 154 from 0 th HD out of 121 possible.  usage down to 1.6182223100602904\n",
      "try to drop unit 154 from 90 th HD out of 121 possible.  usage down to 1.4445782548468882\n",
      "all done trying to reduce usage of unit 154 final usage = 1.39581 0 sec elapsed 19 77 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99811 0.10301\n",
      "starting to reduce usage for unit 593 with usage 1.36793 . Total units tried = 3\n",
      "try to drop unit 593 from 0 th HD out of 129 possible.  usage down to 1.3679335677395794\n",
      "try to drop unit 593 from 90 th HD out of 129 possible.  usage down to 1.311089806912804\n",
      "all done trying to reduce usage of unit 593 final usage = 1.27043 0 sec elapsed 10 81 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 0.99813 0.10222\n",
      "starting to reduce usage for unit 487 with usage 1.33137 . Total units tried = 4\n",
      "try to drop unit 487 from 0 th HD out of 113 possible.  usage down to 1.3313689759042835\n",
      "try to drop unit 487 from 90 th HD out of 113 possible.  usage down to 1.3313689759042835\n",
      "all done trying to reduce usage of unit 487 final usage = 1.33137 0 sec elapsed 0 82 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99813 0.10222\n",
      "starting to reduce usage for unit 149 with usage 1.31749 . Total units tried = 5\n",
      "try to drop unit 149 from 0 th HD out of 105 possible.  usage down to 1.3174915470618986\n",
      "all done trying to reduce usage of unit 149 final usage = 1.2375 1 sec elapsed 8 41 successful, failed patches, couldn't start= 35\n",
      "current avg and SD of usage are 0.99813 0.10181\n",
      "starting to reduce usage for unit 298 with usage 1.312 . Total units tried = 6\n",
      "try to drop unit 298 from 0 th HD out of 94 possible.  usage down to 1.311995950996697\n",
      "all done trying to reduce usage of unit 298 final usage = 1.09568 1 sec elapsed 16 57 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99818 0.10065\n",
      "starting to reduce usage for unit 1466 with usage 1.28783 . Total units tried = 7\n",
      "try to drop unit 1466 from 0 th HD out of 88 possible.  usage down to 1.2878321087559994\n",
      "all done trying to reduce usage of unit 1466 final usage = 1.28783 1 sec elapsed 0 14 successful, failed patches, couldn't start= 57\n",
      "current avg and SD of usage are 0.99818 0.10065\n",
      "starting to reduce usage for unit 473 with usage 1.28743 . Total units tried = 8\n",
      "try to drop unit 473 from 0 th HD out of 97 possible.  usage down to 1.2874293780519401\n",
      "all done trying to reduce usage of unit 473 final usage = 1.26613 2 sec elapsed 2 49 successful, failed patches, couldn't start= 27\n",
      "current avg and SD of usage are 0.99817 0.10054\n",
      "starting to reduce usage for unit 1490 with usage 1.28558 . Total units tried = 9\n",
      "try to drop unit 1490 from 0 th HD out of 87 possible.  usage down to 1.2855835289919335\n",
      "all done trying to reduce usage of unit 1490 final usage = 1.28558 3 sec elapsed 0 44 successful, failed patches, couldn't start= 26\n",
      "current avg and SD of usage are 0.99817 0.10054\n",
      "starting to reduce usage for unit 1266 with usage 1.27243 . Total units tried = 10\n",
      "try to drop unit 1266 from 0 th HD out of 111 possible.  usage down to 1.2724276593275772\n",
      "all done trying to reduce usage of unit 1266 final usage = 1.17835 7 sec elapsed 9 79 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99818 0.10025\n",
      "starting to reduce usage for unit 160 with usage 1.27148 . Total units tried = 11\n",
      "try to drop unit 160 from 0 th HD out of 126 possible.  usage down to 1.271479564128692\n",
      "try to drop unit 160 from 90 th HD out of 126 possible.  usage down to 1.2412747613277577\n",
      "all done trying to reduce usage of unit 160 final usage = 1.24127 9 sec elapsed 3 86 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 0.99816 0.10013\n",
      "starting to reduce usage for unit 568 with usage 1.25577 . Total units tried = 12\n",
      "try to drop unit 568 from 0 th HD out of 105 possible.  usage down to 1.2557730666719884\n",
      "all done trying to reduce usage of unit 568 final usage = 1.01401 9 sec elapsed 21 63 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99816 0.09948\n",
      "starting to reduce usage for unit 378 with usage 1.24952 . Total units tried = 13\n",
      "try to drop unit 378 from 0 th HD out of 99 possible.  usage down to 1.249522350536873\n",
      "all done trying to reduce usage of unit 378 final usage = 1.01771 9 sec elapsed 21 30 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99816 0.09909\n",
      "starting to reduce usage for unit 412 with usage 1.24554 . Total units tried = 14\n",
      "try to drop unit 412 from 0 th HD out of 97 possible.  usage down to 1.2455369946116694\n",
      "all done trying to reduce usage of unit 412 final usage = 1.13024 10 sec elapsed 9 60 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99817 0.09868\n",
      "starting to reduce usage for unit 580 with usage 1.24339 . Total units tried = 15\n",
      "try to drop unit 580 from 0 th HD out of 87 possible.  usage down to 1.2433890975236284\n",
      "all done trying to reduce usage of unit 580 final usage = 1.01775 10 sec elapsed 19 23 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.9982 0.09777\n",
      "starting to reduce usage for unit 556 with usage 1.24047 . Total units tried = 16\n",
      "try to drop unit 556 from 0 th HD out of 90 possible.  usage down to 1.2404692999195088\n",
      "all done trying to reduce usage of unit 556 final usage = 1.15807 10 sec elapsed 5 56 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99819 0.09753\n",
      "starting to reduce usage for unit 1486 with usage 1.23369 . Total units tried = 17\n",
      "try to drop unit 1486 from 0 th HD out of 87 possible.  usage down to 1.2336899997353312\n",
      "all done trying to reduce usage of unit 1486 final usage = 1.21902 11 sec elapsed 1 31 successful, failed patches, couldn't start= 38\n",
      "current avg and SD of usage are 0.9982 0.09736\n",
      "starting to reduce usage for unit 582 with usage 1.23294 . Total units tried = 18\n",
      "try to drop unit 582 from 0 th HD out of 87 possible.  usage down to 1.232943269888152\n",
      "all done trying to reduce usage of unit 582 final usage = 1.01607 12 sec elapsed 17 27 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9982 0.0963\n",
      "starting to reduce usage for unit 101 with usage 1.23195 . Total units tried = 19\n",
      "try to drop unit 101 from 0 th HD out of 82 possible.  usage down to 1.2319532235741304\n",
      "all done trying to reduce usage of unit 101 final usage = 1.1305 12 sec elapsed 7 44 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.99819 0.09606\n",
      "starting to reduce usage for unit 272 with usage 1.2276 . Total units tried = 20\n",
      "try to drop unit 272 from 0 th HD out of 94 possible.  usage down to 1.2275986978371114\n",
      "all done trying to reduce usage of unit 272 final usage = 1.07301 14 sec elapsed 12 55 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99815 0.09573\n",
      "starting to reduce usage for unit 212 with usage 1.22751 . Total units tried = 21\n",
      "try to drop unit 212 from 0 th HD out of 103 possible.  usage down to 1.2275064053841238\n",
      "all done trying to reduce usage of unit 212 final usage = 1.01356 14 sec elapsed 20 59 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99819 0.09357\n",
      "starting to reduce usage for unit 314 with usage 1.22729 . Total units tried = 22\n",
      "try to drop unit 314 from 0 th HD out of 108 possible.  usage down to 1.227288259586276\n",
      "all done trying to reduce usage of unit 314 final usage = 1.07723 15 sec elapsed 12 51 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 0.99816 0.09307\n",
      "starting to reduce usage for unit 214 with usage 1.22625 . Total units tried = 23\n",
      "try to drop unit 214 from 0 th HD out of 92 possible.  usage down to 1.2262478719340872\n",
      "all done trying to reduce usage of unit 214 final usage = 1.20943 15 sec elapsed 1 66 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99817 0.09288\n",
      "starting to reduce usage for unit 1553 with usage 1.22359 . Total units tried = 24\n",
      "try to drop unit 1553 from 0 th HD out of 76 possible.  usage down to 1.2235881712429946\n",
      "all done trying to reduce usage of unit 1553 final usage = 1.22359 17 sec elapsed 0 55 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99817 0.09288\n",
      "starting to reduce usage for unit 1461 with usage 1.22124 . Total units tried = 25\n",
      "try to drop unit 1461 from 0 th HD out of 77 possible.  usage down to 1.221238908802927\n",
      "all done trying to reduce usage of unit 1461 final usage = 1.22124 18 sec elapsed 0 38 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 0.99817 0.09288\n",
      "starting to reduce usage for unit 1552 with usage 1.20973 . Total units tried = 26\n",
      "try to drop unit 1552 from 0 th HD out of 76 possible.  usage down to 1.2097275228465618\n",
      "all done trying to reduce usage of unit 1552 final usage = 1.20973 20 sec elapsed 0 54 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99817 0.09288\n",
      "starting to reduce usage for unit 43 with usage 1.209 . Total units tried = 27\n",
      "try to drop unit 43 from 0 th HD out of 79 possible.  usage down to 1.2089975734455538\n",
      "all done trying to reduce usage of unit 43 final usage = 1.18025 20 sec elapsed 3 51 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.99817 0.0927\n",
      "starting to reduce usage for unit 1512 with usage 1.20548 . Total units tried = 28\n",
      "try to drop unit 1512 from 0 th HD out of 78 possible.  usage down to 1.205482070008427\n",
      "all done trying to reduce usage of unit 1512 final usage = 1.20548 21 sec elapsed 0 55 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99817 0.0927\n",
      "starting to reduce usage for unit 1479 with usage 1.20341 . Total units tried = 29\n",
      "try to drop unit 1479 from 0 th HD out of 83 possible.  usage down to 1.2034096849273734\n",
      "all done trying to reduce usage of unit 1479 final usage = 1.20341 23 sec elapsed 0 52 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.99817 0.0927\n",
      "starting to reduce usage for unit 1460 with usage 1.2023 . Total units tried = 30\n",
      "try to drop unit 1460 from 0 th HD out of 75 possible.  usage down to 1.2023021754913208\n",
      "all done trying to reduce usage of unit 1460 final usage = 1.2023 24 sec elapsed 0 47 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 0.99817 0.0927\n",
      "starting to reduce usage for unit 566 with usage 1.20031 . Total units tried = 31\n",
      "try to drop unit 566 from 0 th HD out of 82 possible.  usage down to 1.20030530241727\n",
      "all done trying to reduce usage of unit 566 final usage = 1.18001 24 sec elapsed 2 61 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99817 0.09258\n",
      "starting to reduce usage for unit 1514 with usage 1.18999 . Total units tried = 32\n",
      "try to drop unit 1514 from 0 th HD out of 73 possible.  usage down to 1.1899937183499532\n",
      "all done trying to reduce usage of unit 1514 final usage = 1.18999 26 sec elapsed 0 45 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99817 0.09258\n",
      "starting to reduce usage for unit 374 with usage 1.18946 . Total units tried = 33\n",
      "try to drop unit 374 from 0 th HD out of 112 possible.  usage down to 1.1894567440781088\n",
      "all done trying to reduce usage of unit 374 final usage = 1.02873 26 sec elapsed 16 52 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 0.99818 0.09244\n",
      "starting to reduce usage for unit 236 with usage 1.1884 . Total units tried = 34\n",
      "try to drop unit 236 from 0 th HD out of 103 possible.  usage down to 1.1883995759799522\n",
      "all done trying to reduce usage of unit 236 final usage = 1.06043 27 sec elapsed 11 44 successful, failed patches, couldn't start= 28\n",
      "current avg and SD of usage are 0.9982 0.09185\n",
      "starting to reduce usage for unit 1488 with usage 1.18276 . Total units tried = 35\n",
      "try to drop unit 1488 from 0 th HD out of 77 possible.  usage down to 1.1827613461237363\n",
      "all done trying to reduce usage of unit 1488 final usage = 1.18276 28 sec elapsed 0 51 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.9982 0.09185\n",
      "starting to reduce usage for unit 477 with usage 1.1818 . Total units tried = 36\n",
      "try to drop unit 477 from 0 th HD out of 77 possible.  usage down to 1.1817964704786859\n",
      "all done trying to reduce usage of unit 477 final usage = 1.10057 28 sec elapsed 8 46 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99827 0.09127\n",
      "starting to reduce usage for unit 1570 with usage 1.17857 . Total units tried = 37\n",
      "try to drop unit 1570 from 0 th HD out of 84 possible.  usage down to 1.1785662346236079\n",
      "all done trying to reduce usage of unit 1570 final usage = 1.17857 30 sec elapsed 0 50 successful, failed patches, couldn't start= 18\n",
      "current avg and SD of usage are 0.99827 0.09127\n",
      "starting to reduce usage for unit 1588 with usage 1.17783 . Total units tried = 38\n",
      "try to drop unit 1588 from 0 th HD out of 60 possible.  usage down to 1.17782789499959\n",
      "all done trying to reduce usage of unit 1588 final usage = 1.17783 32 sec elapsed 0 47 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99827 0.09127\n",
      "starting to reduce usage for unit 1484 with usage 1.17666 . Total units tried = 39\n",
      "try to drop unit 1484 from 0 th HD out of 81 possible.  usage down to 1.17666165400257\n",
      "all done trying to reduce usage of unit 1484 final usage = 1.17666 33 sec elapsed 0 51 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99827 0.09127\n",
      "starting to reduce usage for unit 1477 with usage 1.17659 . Total units tried = 40\n",
      "try to drop unit 1477 from 0 th HD out of 53 possible.  usage down to 1.176586141995544\n",
      "all done trying to reduce usage of unit 1477 final usage = 1.17659 33 sec elapsed 0 29 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99827 0.09127\n",
      "starting to reduce usage for unit 1489 with usage 1.1754 . Total units tried = 41\n",
      "try to drop unit 1489 from 0 th HD out of 76 possible.  usage down to 1.1754031205524968\n",
      "all done trying to reduce usage of unit 1489 final usage = 1.1754 34 sec elapsed 0 22 successful, failed patches, couldn't start= 39\n",
      "current avg and SD of usage are 0.99827 0.09127\n",
      "starting to reduce usage for unit 1304 with usage 1.17408 . Total units tried = 42\n",
      "try to drop unit 1304 from 0 th HD out of 100 possible.  usage down to 1.1740774653185173\n",
      "all done trying to reduce usage of unit 1304 final usage = 1.17408 37 sec elapsed 0 80 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99827 0.09127\n",
      "starting to reduce usage for unit 1465 with usage 1.17245 . Total units tried = 43\n",
      "try to drop unit 1465 from 0 th HD out of 76 possible.  usage down to 1.1724497620564198\n",
      "all done trying to reduce usage of unit 1465 final usage = 1.17245 39 sec elapsed 0 57 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99827 0.09127\n",
      "starting to reduce usage for unit 230 with usage 1.17112 . Total units tried = 44\n",
      "try to drop unit 230 from 0 th HD out of 75 possible.  usage down to 1.1711157165993487\n",
      "all done trying to reduce usage of unit 230 final usage = 1.02815 39 sec elapsed 13 35 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 0.99831 0.09077\n",
      "starting to reduce usage for unit 1462 with usage 1.16834 . Total units tried = 45\n",
      "try to drop unit 1462 from 0 th HD out of 75 possible.  usage down to 1.1683385527862886\n",
      "all done trying to reduce usage of unit 1462 final usage = 1.16834 40 sec elapsed 0 46 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99831 0.09077\n",
      "starting to reduce usage for unit 1637 with usage 1.16477 . Total units tried = 46\n",
      "try to drop unit 1637 from 0 th HD out of 100 possible.  usage down to 1.1647727080111205\n",
      "all done trying to reduce usage of unit 1637 final usage = 1.16477 40 sec elapsed 0 69 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99831 0.09077\n",
      "starting to reduce usage for unit 476 with usage 1.16373 . Total units tried = 47\n",
      "try to drop unit 476 from 0 th HD out of 84 possible.  usage down to 1.1637323203591532\n",
      "all done trying to reduce usage of unit 476 final usage = 1.14439 41 sec elapsed 2 64 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99831 0.09071\n",
      "starting to reduce usage for unit 1636 with usage 1.16338 . Total units tried = 48\n",
      "try to drop unit 1636 from 0 th HD out of 100 possible.  usage down to 1.1633799309930952\n",
      "all done trying to reduce usage of unit 1636 final usage = 1.15036 42 sec elapsed 1 70 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99832 0.09064\n",
      "starting to reduce usage for unit 302 with usage 1.16291 . Total units tried = 49\n",
      "try to drop unit 302 from 0 th HD out of 94 possible.  usage down to 1.1629100785052404\n",
      "all done trying to reduce usage of unit 302 final usage = 1.05956 42 sec elapsed 13 63 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99833 0.0901\n",
      "starting to reduce usage for unit 1139 with usage 1.16283 . Total units tried = 50\n",
      "try to drop unit 1139 from 0 th HD out of 67 possible.  usage down to 1.1628345664980753\n",
      "all done trying to reduce usage of unit 1139 final usage = 1.16283 44 sec elapsed 0 41 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 0.99833 0.0901\n",
      "starting to reduce usage for unit 1587 with usage 1.1619 . Total units tried = 51\n",
      "try to drop unit 1587 from 0 th HD out of 88 possible.  usage down to 1.161903251745072\n",
      "all done trying to reduce usage of unit 1587 final usage = 1.1619 46 sec elapsed 0 52 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 0.99833 0.0901\n",
      "starting to reduce usage for unit 1226 with usage 1.16061 . Total units tried = 52\n",
      "try to drop unit 1226 from 0 th HD out of 100 possible.  usage down to 1.1606111574031233\n",
      "all done trying to reduce usage of unit 1226 final usage = 1.15074 48 sec elapsed 1 77 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99832 0.09009\n",
      "starting to reduce usage for unit 1554 with usage 1.1523 . Total units tried = 53\n",
      "try to drop unit 1554 from 0 th HD out of 76 possible.  usage down to 1.1523048366327793\n",
      "all done trying to reduce usage of unit 1554 final usage = 1.1523 49 sec elapsed 0 39 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 0.99832 0.09009\n",
      "starting to reduce usage for unit 196 with usage 1.15145 . Total units tried = 54\n",
      "try to drop unit 196 from 0 th HD out of 92 possible.  usage down to 1.1514490338867835\n",
      "all done trying to reduce usage of unit 196 final usage = 1.11954 50 sec elapsed 2 63 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99835 0.08994\n",
      "starting to reduce usage for unit 1221 with usage 1.15131 . Total units tried = 55\n",
      "try to drop unit 1221 from 0 th HD out of 96 possible.  usage down to 1.1513147903187528\n",
      "all done trying to reduce usage of unit 1221 final usage = 1.15131 52 sec elapsed 0 49 successful, failed patches, couldn't start= 28\n",
      "current avg and SD of usage are 0.99835 0.08994\n",
      "starting to reduce usage for unit 1159 with usage 1.15027 . Total units tried = 56\n",
      "try to drop unit 1159 from 0 th HD out of 73 possible.  usage down to 1.1502744026666882\n",
      "all done trying to reduce usage of unit 1159 final usage = 1.15027 54 sec elapsed 0 58 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99835 0.08994\n",
      "starting to reduce usage for unit 530 with usage 1.14959 . Total units tried = 57\n",
      "try to drop unit 530 from 0 th HD out of 94 possible.  usage down to 1.149586404380795\n",
      "all done trying to reduce usage of unit 530 final usage = 1.04784 56 sec elapsed 8 58 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.99836 0.08967\n",
      "starting to reduce usage for unit 1223 with usage 1.14928 . Total units tried = 58\n",
      "try to drop unit 1223 from 0 th HD out of 98 possible.  usage down to 1.1492843563527446\n",
      "all done trying to reduce usage of unit 1223 final usage = 1.14928 57 sec elapsed 0 52 successful, failed patches, couldn't start= 27\n",
      "current avg and SD of usage are 0.99836 0.08967\n",
      "starting to reduce usage for unit 1220 with usage 1.14863 . Total units tried = 59\n",
      "try to drop unit 1220 from 0 th HD out of 102 possible.  usage down to 1.1486299189587323\n",
      "all done trying to reduce usage of unit 1220 final usage = 1.14863 60 sec elapsed 0 75 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99836 0.08967\n",
      "starting to reduce usage for unit 1264 with usage 1.14595 . Total units tried = 60\n",
      "try to drop unit 1264 from 0 th HD out of 99 possible.  usage down to 1.1459534378216065\n",
      "all done trying to reduce usage of unit 1264 final usage = 1.14595 63 sec elapsed 0 65 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.99836 0.08967\n",
      "starting to reduce usage for unit 1589 with usage 1.14585 . Total units tried = 61\n",
      "try to drop unit 1589 from 0 th HD out of 67 possible.  usage down to 1.1458527551455695\n",
      "all done trying to reduce usage of unit 1589 final usage = 1.14585 65 sec elapsed 0 49 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99836 0.08967\n",
      "starting to reduce usage for unit 370 with usage 1.14568 . Total units tried = 62\n",
      "try to drop unit 370 from 0 th HD out of 83 possible.  usage down to 1.145684950685539\n",
      "all done trying to reduce usage of unit 370 final usage = 1.01801 66 sec elapsed 9 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99839 0.08957\n",
      "starting to reduce usage for unit 1513 with usage 1.14529 . Total units tried = 63\n",
      "try to drop unit 1513 from 0 th HD out of 74 possible.  usage down to 1.145290610204564\n",
      "all done trying to reduce usage of unit 1513 final usage = 1.14529 67 sec elapsed 0 32 successful, failed patches, couldn't start= 28\n",
      "current avg and SD of usage are 0.99839 0.08957\n",
      "starting to reduce usage for unit 1464 with usage 1.1442 . Total units tried = 64\n",
      "try to drop unit 1464 from 0 th HD out of 81 possible.  usage down to 1.1441998812145648\n",
      "all done trying to reduce usage of unit 1464 final usage = 1.1442 68 sec elapsed 0 54 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99839 0.08957\n",
      "starting to reduce usage for unit 344 with usage 1.1432 . Total units tried = 65\n",
      "try to drop unit 344 from 0 th HD out of 60 possible.  usage down to 1.143201444677407\n",
      "all done trying to reduce usage of unit 344 final usage = 1.10951 69 sec elapsed 2 31 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.9984 0.08943\n",
      "starting to reduce usage for unit 1467 with usage 1.14237 . Total units tried = 66\n",
      "try to drop unit 1467 from 0 th HD out of 80 possible.  usage down to 1.1423708126004775\n",
      "all done trying to reduce usage of unit 1467 final usage = 1.14237 70 sec elapsed 0 47 successful, failed patches, couldn't start= 17\n",
      "current avg and SD of usage are 0.9984 0.08943\n",
      "starting to reduce usage for unit 1568 with usage 1.14125 . Total units tried = 67\n",
      "try to drop unit 1568 from 0 th HD out of 56 possible.  usage down to 1.1412465227184236\n",
      "all done trying to reduce usage of unit 1568 final usage = 1.14125 71 sec elapsed 0 42 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.9984 0.08943\n",
      "starting to reduce usage for unit 1857 with usage 1.13964 . Total units tried = 68\n",
      "try to drop unit 1857 from 0 th HD out of 112 possible.  usage down to 1.1396439901254862\n",
      "all done trying to reduce usage of unit 1857 final usage = 1.12745 73 sec elapsed 1 73 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 0.99839 0.08942\n",
      "starting to reduce usage for unit 1534 with usage 1.1396 . Total units tried = 69\n",
      "try to drop unit 1534 from 0 th HD out of 69 possible.  usage down to 1.139602039010373\n",
      "all done trying to reduce usage of unit 1534 final usage = 1.1396 74 sec elapsed 0 25 successful, failed patches, couldn't start= 31\n",
      "current avg and SD of usage are 0.99839 0.08942\n",
      "starting to reduce usage for unit 551 with usage 1.13912 . Total units tried = 70\n",
      "try to drop unit 551 from 0 th HD out of 76 possible.  usage down to 1.1391154060763884\n",
      "all done trying to reduce usage of unit 551 final usage = 1.03766 74 sec elapsed 8 49 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99839 0.08916\n",
      "starting to reduce usage for unit 1599 with usage 1.13737 . Total units tried = 71\n",
      "try to drop unit 1599 from 0 th HD out of 86 possible.  usage down to 1.1373702396923102\n",
      "all done trying to reduce usage of unit 1599 final usage = 1.13737 75 sec elapsed 0 65 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99839 0.08916\n",
      "starting to reduce usage for unit 1569 with usage 1.13612 . Total units tried = 72\n",
      "try to drop unit 1569 from 0 th HD out of 78 possible.  usage down to 1.1361200964652602\n",
      "all done trying to reduce usage of unit 1569 final usage = 1.13612 77 sec elapsed 0 52 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99839 0.08916\n",
      "starting to reduce usage for unit 1281 with usage 1.13567 . Total units tried = 73\n",
      "try to drop unit 1281 from 0 th HD out of 91 possible.  usage down to 1.1356670244232951\n",
      "all done trying to reduce usage of unit 1281 final usage = 1.13567 79 sec elapsed 0 61 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 0.99839 0.08916\n",
      "starting to reduce usage for unit 1455 with usage 1.13487 . Total units tried = 74\n",
      "try to drop unit 1455 from 0 th HD out of 68 possible.  usage down to 1.1348699532382303\n",
      "all done trying to reduce usage of unit 1455 final usage = 1.13487 81 sec elapsed 0 43 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 0.99839 0.08916\n",
      "starting to reduce usage for unit 1120 with usage 1.13377 . Total units tried = 75\n",
      "try to drop unit 1120 from 0 th HD out of 89 possible.  usage down to 1.1337708340252282\n",
      "all done trying to reduce usage of unit 1120 final usage = 1.13377 82 sec elapsed 0 38 successful, failed patches, couldn't start= 34\n",
      "current avg and SD of usage are 0.99839 0.08916\n",
      "starting to reduce usage for unit 140 with usage 1.13372 . Total units tried = 76\n",
      "try to drop unit 140 from 0 th HD out of 103 possible.  usage down to 1.1337204926873987\n",
      "all done trying to reduce usage of unit 140 final usage = 1.06274 82 sec elapsed 7 57 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 0.9984 0.089\n",
      "starting to reduce usage for unit 180 with usage 1.13244 . Total units tried = 77\n",
      "try to drop unit 180 from 0 th HD out of 90 possible.  usage down to 1.1324367885682485\n",
      "all done trying to reduce usage of unit 180 final usage = 1.01412 83 sec elapsed 11 29 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99839 0.0887\n",
      "starting to reduce usage for unit 621 with usage 1.13209 . Total units tried = 78\n",
      "try to drop unit 621 from 0 th HD out of 78 possible.  usage down to 1.1320927894251165\n",
      "all done trying to reduce usage of unit 621 final usage = 1.04312 84 sec elapsed 6 54 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99845 0.08837\n",
      "starting to reduce usage for unit 1500 with usage 1.13142 . Total units tried = 79\n",
      "try to drop unit 1500 from 0 th HD out of 52 possible.  usage down to 1.1314215715850902\n",
      "all done trying to reduce usage of unit 1500 final usage = 1.13142 85 sec elapsed 0 28 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99845 0.08837\n",
      "starting to reduce usage for unit 1225 with usage 1.12959 . Total units tried = 80\n",
      "try to drop unit 1225 from 0 th HD out of 98 possible.  usage down to 1.1295925029711624\n",
      "all done trying to reduce usage of unit 1225 final usage = 1.12959 86 sec elapsed 0 45 successful, failed patches, couldn't start= 34\n",
      "current avg and SD of usage are 0.99845 0.08837\n",
      "starting to reduce usage for unit 1457 with usage 1.12938 . Total units tried = 81\n",
      "try to drop unit 1457 from 0 th HD out of 68 possible.  usage down to 1.1293827473960683\n",
      "all done trying to reduce usage of unit 1457 final usage = 1.12938 88 sec elapsed 0 50 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99845 0.08837\n",
      "starting to reduce usage for unit 1642 with usage 1.12783 . Total units tried = 82\n",
      "try to drop unit 1642 from 0 th HD out of 75 possible.  usage down to 1.1278305561409823\n",
      "all done trying to reduce usage of unit 1642 final usage = 1.10435 89 sec elapsed 2 44 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99847 0.08831\n",
      "starting to reduce usage for unit 1511 with usage 1.12726 . Total units tried = 83\n",
      "try to drop unit 1511 from 0 th HD out of 51 possible.  usage down to 1.1272600209770125\n",
      "all done trying to reduce usage of unit 1511 final usage = 1.12726 90 sec elapsed 0 26 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.99847 0.08831\n",
      "starting to reduce usage for unit 1480 with usage 1.12678 . Total units tried = 84\n",
      "try to drop unit 1480 from 0 th HD out of 62 possible.  usage down to 1.1267817782660108\n",
      "all done trying to reduce usage of unit 1480 final usage = 1.12678 91 sec elapsed 0 42 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99847 0.08831\n",
      "starting to reduce usage for unit 515 with usage 1.12639 . Total units tried = 85\n",
      "try to drop unit 515 from 0 th HD out of 14 possible.  usage down to 1.1263874377848087\n",
      "all done trying to reduce usage of unit 515 final usage = 1.11028 91 sec elapsed 1 11 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99845 0.08798\n",
      "starting to reduce usage for unit 1265 with usage 1.12607 . Total units tried = 86\n",
      "try to drop unit 1265 from 0 th HD out of 96 possible.  usage down to 1.126068609310993\n",
      "all done trying to reduce usage of unit 1265 final usage = 1.12607 94 sec elapsed 0 65 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 0.99845 0.08798\n",
      "starting to reduce usage for unit 579 with usage 1.12599 . Total units tried = 87\n",
      "try to drop unit 579 from 0 th HD out of 84 possible.  usage down to 1.1259930973039944\n",
      "all done trying to reduce usage of unit 579 final usage = 1.07629 95 sec elapsed 4 39 successful, failed patches, couldn't start= 25\n",
      "current avg and SD of usage are 0.99847 0.08783\n",
      "starting to reduce usage for unit 216 with usage 1.12572 . Total units tried = 88\n",
      "try to drop unit 216 from 0 th HD out of 83 possible.  usage down to 1.1257246101679808\n",
      "all done trying to reduce usage of unit 216 final usage = 1.12572 96 sec elapsed 0 60 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99847 0.08783\n",
      "starting to reduce usage for unit 1189 with usage 1.12572 . Total units tried = 89\n",
      "try to drop unit 1189 from 0 th HD out of 102 possible.  usage down to 1.125716219945037\n",
      "all done trying to reduce usage of unit 1189 final usage = 1.12572 99 sec elapsed 0 76 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99847 0.08783\n",
      "starting to reduce usage for unit 1268 with usage 1.12493 . Total units tried = 90\n",
      "try to drop unit 1268 from 0 th HD out of 95 possible.  usage down to 1.1249275389829931\n",
      "all done trying to reduce usage of unit 1268 final usage = 1.12493 102 sec elapsed 0 76 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99847 0.08783\n",
      "starting to reduce usage for unit 1597 with usage 1.1238 . Total units tried = 91\n",
      "try to drop unit 1597 from 0 th HD out of 78 possible.  usage down to 1.1238032491008896\n",
      "all done trying to reduce usage of unit 1597 final usage = 1.1238 103 sec elapsed 0 59 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99847 0.08783\n",
      "starting to reduce usage for unit 1535 with usage 1.12115 . Total units tried = 92\n",
      "try to drop unit 1535 from 0 th HD out of 63 possible.  usage down to 1.1211519386327702\n",
      "all done trying to reduce usage of unit 1535 final usage = 1.12115 104 sec elapsed 0 45 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99847 0.08783\n",
      "starting to reduce usage for unit 257 with usage 1.12075 . Total units tried = 93\n",
      "try to drop unit 257 from 0 th HD out of 84 possible.  usage down to 1.1207492079286845\n",
      "all done trying to reduce usage of unit 257 final usage = 1.01224 104 sec elapsed 9 21 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99843 0.08767\n",
      "starting to reduce usage for unit 563 with usage 1.12056 . Total units tried = 94\n",
      "try to drop unit 563 from 0 th HD out of 103 possible.  usage down to 1.1205562327999217\n",
      "all done trying to reduce usage of unit 563 final usage = 1.12056 105 sec elapsed 0 60 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 0.99843 0.08767\n",
      "starting to reduce usage for unit 1474 with usage 1.12009 . Total units tried = 95\n",
      "try to drop unit 1474 from 0 th HD out of 75 possible.  usage down to 1.120094770534781\n",
      "all done trying to reduce usage of unit 1474 final usage = 1.12009 106 sec elapsed 0 52 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99843 0.08767\n",
      "starting to reduce usage for unit 532 with usage 1.11997 . Total units tried = 96\n",
      "try to drop unit 532 from 0 th HD out of 93 possible.  usage down to 1.1199689171897547\n",
      "all done trying to reduce usage of unit 532 final usage = 1.09977 107 sec elapsed 2 69 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99844 0.08762\n",
      "starting to reduce usage for unit 419 with usage 1.1189 . Total units tried = 97\n",
      "try to drop unit 419 from 0 th HD out of 102 possible.  usage down to 1.1189033588688178\n",
      "all done trying to reduce usage of unit 419 final usage = 1.03427 107 sec elapsed 9 52 successful, failed patches, couldn't start= 21\n",
      "current avg and SD of usage are 0.99844 0.08757\n",
      "starting to reduce usage for unit 1137 with usage 1.11889 . Total units tried = 98\n",
      "try to drop unit 1137 from 0 th HD out of 61 possible.  usage down to 1.1188949686457261\n",
      "all done trying to reduce usage of unit 1137 final usage = 1.11889 109 sec elapsed 0 47 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99844 0.08757\n",
      "starting to reduce usage for unit 1224 with usage 1.11837 . Total units tried = 99\n",
      "try to drop unit 1224 from 0 th HD out of 95 possible.  usage down to 1.1183663845968026\n",
      "all done trying to reduce usage of unit 1224 final usage = 1.11837 110 sec elapsed 0 38 successful, failed patches, couldn't start= 38\n",
      "current avg and SD of usage are 0.99844 0.08757\n",
      "starting to reduce usage for unit 1185 with usage 1.11701 . Total units tried = 100\n",
      "try to drop unit 1185 from 0 th HD out of 64 possible.  usage down to 1.117007168470638\n",
      "all done trying to reduce usage of unit 1185 final usage = 1.11701 113 sec elapsed 0 51 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99844 0.08757\n",
      "starting to reduce usage for unit 1209 with usage 1.11465 . Total units tried = 101\n",
      "try to drop unit 1209 from 0 th HD out of 65 possible.  usage down to 1.1146495158075442\n",
      "all done trying to reduce usage of unit 1209 final usage = 1.11465 115 sec elapsed 0 44 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99844 0.08757\n",
      "starting to reduce usage for unit 1446 with usage 1.11455 . Total units tried = 102\n",
      "try to drop unit 1446 from 0 th HD out of 60 possible.  usage down to 1.1145488331316007\n",
      "all done trying to reduce usage of unit 1446 final usage = 1.11455 116 sec elapsed 0 47 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99844 0.08757\n",
      "starting to reduce usage for unit 1172 with usage 1.11426 . Total units tried = 103\n",
      "try to drop unit 1172 from 0 th HD out of 65 possible.  usage down to 1.1142551753265209\n",
      "all done trying to reduce usage of unit 1172 final usage = 1.11426 118 sec elapsed 0 50 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99844 0.08757\n",
      "starting to reduce usage for unit 1164 with usage 1.11392 . Total units tried = 104\n",
      "try to drop unit 1164 from 0 th HD out of 65 possible.  usage down to 1.1139195664065296\n",
      "all done trying to reduce usage of unit 1164 final usage = 1.11392 120 sec elapsed 0 43 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99844 0.08757\n",
      "starting to reduce usage for unit 1451 with usage 1.11276 . Total units tried = 105\n",
      "try to drop unit 1451 from 0 th HD out of 68 possible.  usage down to 1.1127617156325407\n",
      "all done trying to reduce usage of unit 1451 final usage = 1.11276 121 sec elapsed 0 34 successful, failed patches, couldn't start= 21\n",
      "current avg and SD of usage are 0.99844 0.08757\n",
      "starting to reduce usage for unit 1303 with usage 1.11246 . Total units tried = 106\n",
      "try to drop unit 1303 from 0 th HD out of 92 possible.  usage down to 1.112459667604586\n",
      "all done trying to reduce usage of unit 1303 final usage = 1.11246 124 sec elapsed 0 68 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99844 0.08757\n",
      "starting to reduce usage for unit 1186 with usage 1.11154 . Total units tried = 107\n",
      "try to drop unit 1186 from 0 th HD out of 98 possible.  usage down to 1.111536743074606\n",
      "all done trying to reduce usage of unit 1186 final usage = 1.10332 125 sec elapsed 1 42 successful, failed patches, couldn't start= 36\n",
      "current avg and SD of usage are 0.99843 0.08756\n",
      "starting to reduce usage for unit 499 with usage 1.11125 . Total units tried = 108\n",
      "try to drop unit 499 from 0 th HD out of 100 possible.  usage down to 1.111251475492658\n",
      "all done trying to reduce usage of unit 499 final usage = 1.10422 125 sec elapsed 1 38 successful, failed patches, couldn't start= 41\n",
      "current avg and SD of usage are 0.99843 0.08754\n",
      "starting to reduce usage for unit 1631 with usage 1.10941 . Total units tried = 109\n",
      "try to drop unit 1631 from 0 th HD out of 44 possible.  usage down to 1.109405626432434\n",
      "all done trying to reduce usage of unit 1631 final usage = 1.05184 126 sec elapsed 4 32 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99839 0.08752\n",
      "starting to reduce usage for unit 1478 with usage 1.10823 . Total units tried = 110\n",
      "try to drop unit 1478 from 0 th HD out of 49 possible.  usage down to 1.108230995212431\n",
      "all done trying to reduce usage of unit 1478 final usage = 1.10823 127 sec elapsed 0 28 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 0.99839 0.08752\n",
      "starting to reduce usage for unit 183 with usage 1.10804 . Total units tried = 111\n",
      "try to drop unit 183 from 0 th HD out of 95 possible.  usage down to 1.1080380200833924\n",
      "all done trying to reduce usage of unit 183 final usage = 1.10804 128 sec elapsed 0 71 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99839 0.08752\n",
      "starting to reduce usage for unit 1954 with usage 1.1068 . Total units tried = 112\n",
      "try to drop unit 1954 from 0 th HD out of 116 possible.  usage down to 1.106804657302491\n",
      "try to drop unit 1954 from 90 th HD out of 116 possible.  usage down to 1.0990437010272462\n",
      "all done trying to reduce usage of unit 1954 final usage = 1.09904 130 sec elapsed 1 81 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99838 0.08751\n",
      "starting to reduce usage for unit 1475 with usage 1.10671 . Total units tried = 113\n",
      "try to drop unit 1475 from 0 th HD out of 75 possible.  usage down to 1.1067123648493553\n",
      "all done trying to reduce usage of unit 1475 final usage = 1.10671 131 sec elapsed 0 54 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99838 0.08751\n",
      "starting to reduce usage for unit 1173 with usage 1.10624 . Total units tried = 114\n",
      "try to drop unit 1173 from 0 th HD out of 71 possible.  usage down to 1.1062425123612794\n",
      "all done trying to reduce usage of unit 1173 final usage = 1.10624 134 sec elapsed 0 53 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99838 0.08751\n",
      "starting to reduce usage for unit 1523 with usage 1.10579 . Total units tried = 115\n",
      "try to drop unit 1523 from 0 th HD out of 52 possible.  usage down to 1.1057894403193218\n",
      "all done trying to reduce usage of unit 1523 final usage = 1.10579 135 sec elapsed 0 31 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99838 0.08751\n",
      "starting to reduce usage for unit 1210 with usage 1.10567 . Total units tried = 116\n",
      "try to drop unit 1210 from 0 th HD out of 57 possible.  usage down to 1.1056719771972683\n",
      "all done trying to reduce usage of unit 1210 final usage = 1.10567 137 sec elapsed 0 43 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99838 0.08751\n",
      "starting to reduce usage for unit 1187 with usage 1.10476 . Total units tried = 117\n",
      "try to drop unit 1187 from 0 th HD out of 99 possible.  usage down to 1.104757442890385\n",
      "all done trying to reduce usage of unit 1187 final usage = 1.10476 140 sec elapsed 0 77 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99838 0.08751\n",
      "starting to reduce usage for unit 1110 with usage 1.1043 . Total units tried = 118\n",
      "try to drop unit 1110 from 0 th HD out of 91 possible.  usage down to 1.1042959806253185\n",
      "all done trying to reduce usage of unit 1110 final usage = 1.1043 144 sec elapsed 0 62 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99838 0.08751\n",
      "starting to reduce usage for unit 1473 with usage 1.10331 . Total units tried = 119\n",
      "try to drop unit 1473 from 0 th HD out of 62 possible.  usage down to 1.1033143245342414\n",
      "all done trying to reduce usage of unit 1473 final usage = 1.10331 145 sec elapsed 0 45 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99838 0.08751\n",
      "starting to reduce usage for unit 1108 with usage 1.10284 . Total units tried = 120\n",
      "try to drop unit 1108 from 0 th HD out of 88 possible.  usage down to 1.1028444720462947\n",
      "all done trying to reduce usage of unit 1108 final usage = 1.10284 147 sec elapsed 0 47 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 0.99838 0.08751\n",
      "starting to reduce usage for unit 1540 with usage 1.1025 . Total units tried = 121\n",
      "try to drop unit 1540 from 0 th HD out of 74 possible.  usage down to 1.102500472903179\n",
      "all done trying to reduce usage of unit 1540 final usage = 1.1025 148 sec elapsed 0 50 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.99838 0.08751\n",
      "starting to reduce usage for unit 601 with usage 1.10247 . Total units tried = 122\n",
      "try to drop unit 601 from 0 th HD out of 83 possible.  usage down to 1.1024669120111061\n",
      "all done trying to reduce usage of unit 601 final usage = 1.01804 149 sec elapsed 7 28 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99839 0.08742\n",
      "starting to reduce usage for unit 1449 with usage 1.10081 . Total units tried = 123\n",
      "try to drop unit 1449 from 0 th HD out of 70 possible.  usage down to 1.1008140380801705\n",
      "all done trying to reduce usage of unit 1449 final usage = 1.10081 150 sec elapsed 0 43 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 0.99839 0.08742\n",
      "starting to reduce usage for unit 519 with usage 1.09815 . Total units tried = 124\n",
      "try to drop unit 519 from 0 th HD out of 86 possible.  usage down to 1.0981459471661783\n",
      "all done trying to reduce usage of unit 519 final usage = 1.08276 151 sec elapsed 2 57 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.99839 0.08737\n",
      "starting to reduce usage for unit 1157 with usage 1.0976 . Total units tried = 125\n",
      "try to drop unit 1157 from 0 th HD out of 94 possible.  usage down to 1.0976005826711068\n",
      "all done trying to reduce usage of unit 1157 final usage = 1.0976 154 sec elapsed 0 60 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 0.99839 0.08737\n",
      "starting to reduce usage for unit 1600 with usage 1.09749 . Total units tried = 126\n",
      "try to drop unit 1600 from 0 th HD out of 92 possible.  usage down to 1.0974915097721114\n",
      "all done trying to reduce usage of unit 1600 final usage = 1.09749 154 sec elapsed 0 61 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 0.99839 0.08737\n",
      "starting to reduce usage for unit 1752 with usage 1.0963 . Total units tried = 127\n",
      "try to drop unit 1752 from 0 th HD out of 80 possible.  usage down to 1.0963000981058617\n",
      "all done trying to reduce usage of unit 1752 final usage = 1.0732 157 sec elapsed 2 55 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99838 0.08736\n",
      "starting to reduce usage for unit 295 with usage 1.09604 . Total units tried = 128\n",
      "try to drop unit 295 from 0 th HD out of 83 possible.  usage down to 1.0960400011930351\n",
      "all done trying to reduce usage of unit 295 final usage = 1.01807 158 sec elapsed 6 54 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99838 0.08726\n",
      "starting to reduce usage for unit 130 with usage 1.09529 . Total units tried = 129\n",
      "try to drop unit 130 from 0 th HD out of 78 possible.  usage down to 1.0952932713458763\n",
      "all done trying to reduce usage of unit 130 final usage = 1.01109 158 sec elapsed 6 2 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99836 0.0872\n",
      "starting to reduce usage for unit 456 with usage 1.09431 . Total units tried = 130\n",
      "try to drop unit 456 from 0 th HD out of 78 possible.  usage down to 1.0943116152550099\n",
      "all done trying to reduce usage of unit 456 final usage = 1.07714 160 sec elapsed 1 57 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99836 0.08719\n",
      "starting to reduce usage for unit 1267 with usage 1.09275 . Total units tried = 131\n",
      "try to drop unit 1267 from 0 th HD out of 92 possible.  usage down to 1.092751033776979\n",
      "all done trying to reduce usage of unit 1267 final usage = 1.09275 163 sec elapsed 0 74 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99836 0.08719\n",
      "starting to reduce usage for unit 1469 with usage 1.09245 . Total units tried = 132\n",
      "try to drop unit 1469 from 0 th HD out of 67 possible.  usage down to 1.092448985748912\n",
      "all done trying to reduce usage of unit 1469 final usage = 1.09245 164 sec elapsed 0 25 successful, failed patches, couldn't start= 29\n",
      "current avg and SD of usage are 0.99836 0.08719\n",
      "starting to reduce usage for unit 1560 with usage 1.09146 . Total units tried = 133\n",
      "try to drop unit 1560 from 0 th HD out of 92 possible.  usage down to 1.0914589394349243\n",
      "all done trying to reduce usage of unit 1560 final usage = 1.09146 164 sec elapsed 0 67 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99836 0.08719\n",
      "starting to reduce usage for unit 1282 with usage 1.09072 . Total units tried = 134\n",
      "try to drop unit 1282 from 0 th HD out of 90 possible.  usage down to 1.0907205998108664\n",
      "all done trying to reduce usage of unit 1282 final usage = 1.09072 167 sec elapsed 0 61 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99836 0.08719\n",
      "starting to reduce usage for unit 1610 with usage 1.09049 . Total units tried = 135\n",
      "try to drop unit 1610 from 0 th HD out of 93 possible.  usage down to 1.0904856735668225\n",
      "all done trying to reduce usage of unit 1610 final usage = 1.09049 167 sec elapsed 0 52 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 0.99836 0.08719\n",
      "starting to reduce usage for unit 1625 with usage 1.09042 . Total units tried = 136\n",
      "try to drop unit 1625 from 0 th HD out of 84 possible.  usage down to 1.0904185517828202\n",
      "all done trying to reduce usage of unit 1625 final usage = 1.09042 168 sec elapsed 0 53 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.99836 0.08719\n",
      "starting to reduce usage for unit 1626 with usage 1.08981 . Total units tried = 137\n",
      "try to drop unit 1626 from 0 th HD out of 77 possible.  usage down to 1.0898144557267944\n",
      "all done trying to reduce usage of unit 1626 final usage = 1.08981 169 sec elapsed 0 43 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 0.99836 0.08719\n",
      "starting to reduce usage for unit 1128 with usage 1.08975 . Total units tried = 138\n",
      "try to drop unit 1128 from 0 th HD out of 89 possible.  usage down to 1.0897473339428587\n",
      "all done trying to reduce usage of unit 1128 final usage = 1.08975 171 sec elapsed 0 32 successful, failed patches, couldn't start= 40\n",
      "current avg and SD of usage are 0.99836 0.08719\n",
      "starting to reduce usage for unit 1585 with usage 1.08842 . Total units tried = 139\n",
      "try to drop unit 1585 from 0 th HD out of 70 possible.  usage down to 1.0884216787087877\n",
      "all done trying to reduce usage of unit 1585 final usage = 1.08842 172 sec elapsed 0 54 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99836 0.08719\n",
      "starting to reduce usage for unit 1472 with usage 1.08741 . Total units tried = 140\n",
      "try to drop unit 1472 from 0 th HD out of 67 possible.  usage down to 1.087414851948756\n",
      "all done trying to reduce usage of unit 1472 final usage = 1.08741 173 sec elapsed 0 45 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99836 0.08719\n",
      "starting to reduce usage for unit 1607 with usage 1.08726 . Total units tried = 141\n",
      "try to drop unit 1607 from 0 th HD out of 97 possible.  usage down to 1.087255437711757\n",
      "all done trying to reduce usage of unit 1607 final usage = 1.08726 174 sec elapsed 0 66 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 0.99836 0.08719\n",
      "starting to reduce usage for unit 1174 with usage 1.08725 . Total units tried = 142\n",
      "try to drop unit 1174 from 0 th HD out of 79 possible.  usage down to 1.0872470474886788\n",
      "all done trying to reduce usage of unit 1174 final usage = 1.08725 177 sec elapsed 0 53 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99836 0.08719\n",
      "starting to reduce usage for unit 231 with usage 1.08724 . Total units tried = 143\n",
      "try to drop unit 231 from 0 th HD out of 88 possible.  usage down to 1.0872386572657944\n",
      "all done trying to reduce usage of unit 231 final usage = 1.015 177 sec elapsed 6 47 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99837 0.08715\n",
      "starting to reduce usage for unit 1125 with usage 1.08721 . Total units tried = 144\n",
      "try to drop unit 1125 from 0 th HD out of 84 possible.  usage down to 1.0872134865968062\n",
      "all done trying to reduce usage of unit 1125 final usage = 1.08721 180 sec elapsed 0 60 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99837 0.08715\n",
      "starting to reduce usage for unit 1608 with usage 1.08598 . Total units tried = 145\n",
      "try to drop unit 1608 from 0 th HD out of 70 possible.  usage down to 1.085980123815685\n",
      "all done trying to reduce usage of unit 1608 final usage = 1.08598 181 sec elapsed 0 55 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99837 0.08715\n",
      "starting to reduce usage for unit 1564 with usage 1.08524 . Total units tried = 146\n",
      "try to drop unit 1564 from 0 th HD out of 75 possible.  usage down to 1.0852417841917208\n",
      "all done trying to reduce usage of unit 1564 final usage = 1.08524 182 sec elapsed 0 55 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99837 0.08715\n",
      "starting to reduce usage for unit 227 with usage 1.08507 . Total units tried = 147\n",
      "try to drop unit 227 from 0 th HD out of 79 possible.  usage down to 1.0850739797317197\n",
      "all done trying to reduce usage of unit 227 final usage = 1.08507 182 sec elapsed 0 63 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99837 0.08715\n",
      "starting to reduce usage for unit 67 with usage 1.08465 . Total units tried = 148\n",
      "try to drop unit 67 from 0 th HD out of 76 possible.  usage down to 1.084646078358757\n",
      "all done trying to reduce usage of unit 67 final usage = 1.01747 182 sec elapsed 7 33 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99835 0.08701\n",
      "starting to reduce usage for unit 1476 with usage 1.08464 . Total units tried = 149\n",
      "try to drop unit 1476 from 0 th HD out of 67 possible.  usage down to 1.0846376881356843\n",
      "all done trying to reduce usage of unit 1476 final usage = 1.08464 183 sec elapsed 0 45 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99835 0.08701\n",
      "starting to reduce usage for unit 1494 with usage 1.08411 . Total units tried = 150\n",
      "try to drop unit 1494 from 0 th HD out of 79 possible.  usage down to 1.0841091040866904\n",
      "all done trying to reduce usage of unit 1494 final usage = 1.08411 184 sec elapsed 0 24 successful, failed patches, couldn't start= 40\n",
      "current avg and SD of usage are 0.99835 0.08701\n",
      "starting to reduce usage for unit 1542 with usage 1.08408 . Total units tried = 151\n",
      "try to drop unit 1542 from 0 th HD out of 74 possible.  usage down to 1.0840755431946287\n",
      "all done trying to reduce usage of unit 1542 final usage = 1.08408 185 sec elapsed 0 54 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99835 0.08701\n",
      "starting to reduce usage for unit 1183 with usage 1.0836 . Total units tried = 152\n",
      "try to drop unit 1183 from 0 th HD out of 94 possible.  usage down to 1.0835973004836406\n",
      "all done trying to reduce usage of unit 1183 final usage = 1.0836 189 sec elapsed 0 54 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 0.99835 0.08701\n",
      "starting to reduce usage for unit 1652 with usage 1.08356 . Total units tried = 153\n",
      "try to drop unit 1652 from 0 th HD out of 98 possible.  usage down to 1.0835553493686234\n",
      "all done trying to reduce usage of unit 1652 final usage = 1.08356 190 sec elapsed 0 54 successful, failed patches, couldn't start= 25\n",
      "current avg and SD of usage are 0.99835 0.08701\n",
      "starting to reduce usage for unit 347 with usage 1.08303 . Total units tried = 154\n",
      "try to drop unit 347 from 0 th HD out of 79 possible.  usage down to 1.0830267653195917\n",
      "all done trying to reduce usage of unit 347 final usage = 1.01014 190 sec elapsed 5 35 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99835 0.08698\n",
      "starting to reduce usage for unit 1537 with usage 1.08282 . Total units tried = 155\n",
      "try to drop unit 1537 from 0 th HD out of 63 possible.  usage down to 1.0828170097445575\n",
      "all done trying to reduce usage of unit 1537 final usage = 1.08282 191 sec elapsed 0 29 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 0.99835 0.08698\n",
      "starting to reduce usage for unit 1530 with usage 1.0821 . Total units tried = 156\n",
      "try to drop unit 1530 from 0 th HD out of 77 possible.  usage down to 1.0821038407896122\n",
      "all done trying to reduce usage of unit 1530 final usage = 1.07561 192 sec elapsed 1 58 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99835 0.08698\n",
      "starting to reduce usage for unit 1577 with usage 1.08205 . Total units tried = 157\n",
      "try to drop unit 1577 from 0 th HD out of 81 possible.  usage down to 1.0820534994515536\n",
      "all done trying to reduce usage of unit 1577 final usage = 1.08205 193 sec elapsed 0 57 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99835 0.08698\n",
      "starting to reduce usage for unit 1584 with usage 1.0818 . Total units tried = 158\n",
      "try to drop unit 1584 from 0 th HD out of 63 possible.  usage down to 1.0818017927615842\n",
      "all done trying to reduce usage of unit 1584 final usage = 1.0818 194 sec elapsed 0 50 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99835 0.08698\n",
      "starting to reduce usage for unit 1635 with usage 1.08178 . Total units tried = 159\n",
      "try to drop unit 1635 from 0 th HD out of 66 possible.  usage down to 1.0817766220925449\n",
      "all done trying to reduce usage of unit 1635 final usage = 1.08178 194 sec elapsed 0 43 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.99835 0.08698\n",
      "starting to reduce usage for unit 1586 with usage 1.08134 . Total units tried = 160\n",
      "try to drop unit 1586 from 0 th HD out of 73 possible.  usage down to 1.0813403304965863\n",
      "all done trying to reduce usage of unit 1586 final usage = 1.08134 195 sec elapsed 0 55 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99835 0.08698\n",
      "starting to reduce usage for unit 1305 with usage 1.08099 . Total units tried = 161\n",
      "try to drop unit 1305 from 0 th HD out of 91 possible.  usage down to 1.0809879411306071\n",
      "all done trying to reduce usage of unit 1305 final usage = 1.08099 198 sec elapsed 0 73 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99835 0.08698\n",
      "starting to reduce usage for unit 1645 with usage 1.08054 . Total units tried = 162\n",
      "try to drop unit 1645 from 0 th HD out of 70 possible.  usage down to 1.080543259311517\n",
      "all done trying to reduce usage of unit 1645 final usage = 1.08054 199 sec elapsed 0 44 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 0.99835 0.08698\n",
      "starting to reduce usage for unit 1646 with usage 1.07927 . Total units tried = 163\n",
      "try to drop unit 1646 from 0 th HD out of 93 possible.  usage down to 1.079267945415509\n",
      "all done trying to reduce usage of unit 1646 final usage = 1.07927 200 sec elapsed 0 53 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 0.99835 0.08698\n",
      "starting to reduce usage for unit 1609 with usage 1.0792 . Total units tried = 164\n",
      "try to drop unit 1609 from 0 th HD out of 80 possible.  usage down to 1.0792008236314736\n",
      "all done trying to reduce usage of unit 1609 final usage = 1.0792 201 sec elapsed 0 50 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99835 0.08698\n",
      "starting to reduce usage for unit 594 with usage 1.07881 . Total units tried = 165\n",
      "try to drop unit 594 from 0 th HD out of 96 possible.  usage down to 1.0788148733735194\n",
      "all done trying to reduce usage of unit 594 final usage = 1.06417 202 sec elapsed 1 61 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.99834 0.08697\n",
      "starting to reduce usage for unit 1263 with usage 1.0785 . Total units tried = 166\n",
      "try to drop unit 1263 from 0 th HD out of 88 possible.  usage down to 1.0784960448995367\n",
      "all done trying to reduce usage of unit 1263 final usage = 1.0785 203 sec elapsed 0 37 successful, failed patches, couldn't start= 34\n",
      "current avg and SD of usage are 0.99834 0.08697\n",
      "starting to reduce usage for unit 1576 with usage 1.0781 . Total units tried = 167\n",
      "try to drop unit 1576 from 0 th HD out of 70 possible.  usage down to 1.0781017044184305\n",
      "all done trying to reduce usage of unit 1576 final usage = 1.0781 204 sec elapsed 0 50 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99834 0.08697\n",
      "starting to reduce usage for unit 358 with usage 1.07808 . Total units tried = 168\n",
      "try to drop unit 358 from 0 th HD out of 94 possible.  usage down to 1.0780765337496077\n",
      "all done trying to reduce usage of unit 358 final usage = 1.01403 204 sec elapsed 6 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99831 0.08689\n",
      "starting to reduce usage for unit 1595 with usage 1.07791 . Total units tried = 169\n",
      "try to drop unit 1595 from 0 th HD out of 79 possible.  usage down to 1.077908729289441\n",
      "all done trying to reduce usage of unit 1595 final usage = 1.07791 205 sec elapsed 0 50 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99831 0.08689\n",
      "starting to reduce usage for unit 1249 with usage 1.07628 . Total units tried = 170\n",
      "try to drop unit 1249 from 0 th HD out of 86 possible.  usage down to 1.0762810260274391\n",
      "all done trying to reduce usage of unit 1249 final usage = 1.07628 207 sec elapsed 0 63 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99831 0.08689\n",
      "starting to reduce usage for unit 542 with usage 1.07621 . Total units tried = 171\n",
      "try to drop unit 542 from 0 th HD out of 79 possible.  usage down to 1.0762055140205016\n",
      "all done trying to reduce usage of unit 542 final usage = 1.01686 208 sec elapsed 5 28 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99829 0.08681\n",
      "starting to reduce usage for unit 721 with usage 1.07594 . Total units tried = 172\n",
      "try to drop unit 721 from 0 th HD out of 72 possible.  usage down to 1.0759370268844666\n",
      "all done trying to reduce usage of unit 721 final usage = 1.07594 209 sec elapsed 0 51 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99829 0.08681\n",
      "starting to reduce usage for unit 273 with usage 1.07579 . Total units tried = 173\n",
      "try to drop unit 273 from 0 th HD out of 76 possible.  usage down to 1.0757943930933793\n",
      "all done trying to reduce usage of unit 273 final usage = 1.06408 210 sec elapsed 1 54 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99828 0.08679\n",
      "starting to reduce usage for unit 8 with usage 1.07543 . Total units tried = 174\n",
      "try to drop unit 8 from 0 th HD out of 87 possible.  usage down to 1.0754336135043434\n",
      "all done trying to reduce usage of unit 8 final usage = 1.07543 210 sec elapsed 0 70 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99828 0.08679\n",
      "starting to reduce usage for unit 1651 with usage 1.07532 . Total units tried = 175\n",
      "try to drop unit 1651 from 0 th HD out of 67 possible.  usage down to 1.0753161503823743\n",
      "all done trying to reduce usage of unit 1651 final usage = 1.07532 211 sec elapsed 0 45 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99828 0.08679\n",
      "starting to reduce usage for unit 1606 with usage 1.07469 . Total units tried = 176\n",
      "try to drop unit 1606 from 0 th HD out of 94 possible.  usage down to 1.0746868836573296\n",
      "all done trying to reduce usage of unit 1606 final usage = 1.07469 213 sec elapsed 0 65 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99828 0.08679\n",
      "starting to reduce usage for unit 1124 with usage 1.07461 . Total units tried = 177\n",
      "try to drop unit 1124 from 0 th HD out of 86 possible.  usage down to 1.0746113716504127\n",
      "all done trying to reduce usage of unit 1124 final usage = 1.07461 214 sec elapsed 0 31 successful, failed patches, couldn't start= 38\n",
      "current avg and SD of usage are 0.99828 0.08679\n",
      "starting to reduce usage for unit 350 with usage 1.07435 . Total units tried = 178\n",
      "try to drop unit 350 from 0 th HD out of 83 possible.  usage down to 1.0743512747372865\n",
      "all done trying to reduce usage of unit 350 final usage = 1.01579 215 sec elapsed 5 29 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99829 0.08672\n",
      "starting to reduce usage for unit 1485 with usage 1.07405 . Total units tried = 179\n",
      "try to drop unit 1485 from 0 th HD out of 79 possible.  usage down to 1.074049226709386\n",
      "all done trying to reduce usage of unit 1485 final usage = 1.07405 215 sec elapsed 0 5 successful, failed patches, couldn't start= 59\n",
      "current avg and SD of usage are 0.99829 0.08672\n",
      "starting to reduce usage for unit 1644 with usage 1.07368 . Total units tried = 180\n",
      "try to drop unit 1644 from 0 th HD out of 62 possible.  usage down to 1.0736800568973022\n",
      "all done trying to reduce usage of unit 1644 final usage = 1.07368 216 sec elapsed 0 27 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 0.99829 0.08672\n",
      "starting to reduce usage for unit 1188 with usage 1.07295 . Total units tried = 181\n",
      "try to drop unit 1188 from 0 th HD out of 93 possible.  usage down to 1.0729501074963965\n",
      "all done trying to reduce usage of unit 1188 final usage = 1.06348 219 sec elapsed 1 53 successful, failed patches, couldn't start= 21\n",
      "current avg and SD of usage are 0.99828 0.08671\n",
      "starting to reduce usage for unit 1138 with usage 1.07294 . Total units tried = 182\n",
      "try to drop unit 1138 from 0 th HD out of 60 possible.  usage down to 1.072941717273298\n",
      "all done trying to reduce usage of unit 1138 final usage = 1.07294 221 sec elapsed 0 41 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99828 0.08671\n",
      "starting to reduce usage for unit 1966 with usage 1.07287 . Total units tried = 183\n",
      "try to drop unit 1966 from 0 th HD out of 85 possible.  usage down to 1.0728745954891528\n",
      "all done trying to reduce usage of unit 1966 final usage = 1.0144 222 sec elapsed 6 42 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99828 0.08667\n",
      "starting to reduce usage for unit 1574 with usage 1.07268 . Total units tried = 184\n",
      "try to drop unit 1574 from 0 th HD out of 70 possible.  usage down to 1.0726816203602543\n",
      "all done trying to reduce usage of unit 1574 final usage = 1.07268 223 sec elapsed 0 54 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99828 0.08667\n",
      "starting to reduce usage for unit 722 with usage 1.07266 . Total units tried = 185\n",
      "try to drop unit 722 from 0 th HD out of 81 possible.  usage down to 1.0726564496913373\n",
      "all done trying to reduce usage of unit 722 final usage = 1.06085 225 sec elapsed 1 62 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1250 with usage 1.07212 . Total units tried = 186\n",
      "try to drop unit 1250 from 0 th HD out of 78 possible.  usage down to 1.0721194754192933\n",
      "all done trying to reduce usage of unit 1250 final usage = 1.07212 227 sec elapsed 0 59 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1547 with usage 1.07159 . Total units tried = 187\n",
      "try to drop unit 1547 from 0 th HD out of 40 possible.  usage down to 1.0715908913702856\n",
      "all done trying to reduce usage of unit 1547 final usage = 1.07159 228 sec elapsed 0 20 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1627 with usage 1.07129 . Total units tried = 188\n",
      "try to drop unit 1627 from 0 th HD out of 84 possible.  usage down to 1.0712888433422334\n",
      "all done trying to reduce usage of unit 1627 final usage = 1.07129 229 sec elapsed 0 54 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1501 with usage 1.07126 . Total units tried = 189\n",
      "try to drop unit 1501 from 0 th HD out of 53 possible.  usage down to 1.0712552824502446\n",
      "all done trying to reduce usage of unit 1501 final usage = 1.07126 230 sec elapsed 0 36 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1563 with usage 1.0712 . Total units tried = 190\n",
      "try to drop unit 1563 from 0 th HD out of 91 possible.  usage down to 1.0711965508892851\n",
      "all done trying to reduce usage of unit 1563 final usage = 1.0712 231 sec elapsed 0 69 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1832 with usage 1.071 . Total units tried = 191\n",
      "try to drop unit 1832 from 0 th HD out of 105 possible.  usage down to 1.071003575760371\n",
      "all done trying to reduce usage of unit 1832 final usage = 1.071 232 sec elapsed 0 15 successful, failed patches, couldn't start= 69\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1447 with usage 1.07095 . Total units tried = 192\n",
      "try to drop unit 1447 from 0 th HD out of 67 possible.  usage down to 1.0709532344222474\n",
      "all done trying to reduce usage of unit 1447 final usage = 1.07095 233 sec elapsed 0 17 successful, failed patches, couldn't start= 37\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1617 with usage 1.07079 . Total units tried = 193\n",
      "try to drop unit 1617 from 0 th HD out of 65 possible.  usage down to 1.0707854299622366\n",
      "all done trying to reduce usage of unit 1617 final usage = 1.07079 233 sec elapsed 0 52 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1130 with usage 1.0705 . Total units tried = 194\n",
      "try to drop unit 1130 from 0 th HD out of 81 possible.  usage down to 1.0705001623802812\n",
      "all done trying to reduce usage of unit 1130 final usage = 1.0705 236 sec elapsed 0 55 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1165 with usage 1.06988 . Total units tried = 195\n",
      "try to drop unit 1165 from 0 th HD out of 53 possible.  usage down to 1.069879285878148\n",
      "all done trying to reduce usage of unit 1165 final usage = 1.06988 238 sec elapsed 0 42 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1632 with usage 1.06961 . Total units tried = 196\n",
      "try to drop unit 1632 from 0 th HD out of 68 possible.  usage down to 1.0696107987421632\n",
      "all done trying to reduce usage of unit 1632 final usage = 1.06961 239 sec elapsed 0 49 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1507 with usage 1.06959 . Total units tried = 197\n",
      "try to drop unit 1507 from 0 th HD out of 75 possible.  usage down to 1.0695856280731972\n",
      "all done trying to reduce usage of unit 1507 final usage = 1.06959 240 sec elapsed 0 51 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1481 with usage 1.06933 . Total units tried = 198\n",
      "try to drop unit 1481 from 0 th HD out of 43 possible.  usage down to 1.0693339213832054\n",
      "all done trying to reduce usage of unit 1481 final usage = 1.06933 240 sec elapsed 0 23 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1503 with usage 1.06922 . Total units tried = 199\n",
      "try to drop unit 1503 from 0 th HD out of 65 possible.  usage down to 1.0692248484841393\n",
      "all done trying to reduce usage of unit 1503 final usage = 1.06922 241 sec elapsed 0 33 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1129 with usage 1.06878 . Total units tried = 200\n",
      "try to drop unit 1129 from 0 th HD out of 86 possible.  usage down to 1.0687801666652281\n",
      "all done trying to reduce usage of unit 1129 final usage = 1.06878 245 sec elapsed 0 52 successful, failed patches, couldn't start= 17\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1619 with usage 1.06877 . Total units tried = 201\n",
      "try to drop unit 1619 from 0 th HD out of 76 possible.  usage down to 1.0687717764421634\n",
      "all done trying to reduce usage of unit 1619 final usage = 1.05419 245 sec elapsed 1 14 successful, failed patches, couldn't start= 46\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1448 with usage 1.06875 . Total units tried = 202\n",
      "try to drop unit 1448 from 0 th HD out of 74 possible.  usage down to 1.0687549959962073\n",
      "all done trying to reduce usage of unit 1448 final usage = 1.06875 246 sec elapsed 0 31 successful, failed patches, couldn't start= 29\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1640 with usage 1.06851 . Total units tried = 203\n",
      "try to drop unit 1640 from 0 th HD out of 80 possible.  usage down to 1.0685116795291436\n",
      "all done trying to reduce usage of unit 1640 final usage = 1.06851 247 sec elapsed 0 42 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1649 with usage 1.06813 . Total units tried = 204\n",
      "try to drop unit 1649 from 0 th HD out of 71 possible.  usage down to 1.0681341194941505\n",
      "all done trying to reduce usage of unit 1649 final usage = 1.06813 248 sec elapsed 0 27 successful, failed patches, couldn't start= 30\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1499 with usage 1.06808 . Total units tried = 205\n",
      "try to drop unit 1499 from 0 th HD out of 51 possible.  usage down to 1.0680837781561405\n",
      "all done trying to reduce usage of unit 1499 final usage = 1.06808 249 sec elapsed 0 36 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1285 with usage 1.06805 . Total units tried = 206\n",
      "try to drop unit 1285 from 0 th HD out of 85 possible.  usage down to 1.0680502172641946\n",
      "all done trying to reduce usage of unit 1285 final usage = 1.06805 250 sec elapsed 0 39 successful, failed patches, couldn't start= 29\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1877 with usage 1.0679 . Total units tried = 207\n",
      "try to drop unit 1877 from 0 th HD out of 108 possible.  usage down to 1.0678991932502409\n",
      "all done trying to reduce usage of unit 1877 final usage = 1.0679 252 sec elapsed 0 65 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1184 with usage 1.06772 . Total units tried = 208\n",
      "try to drop unit 1184 from 0 th HD out of 54 possible.  usage down to 1.067722998567106\n",
      "all done trying to reduce usage of unit 1184 final usage = 1.06772 254 sec elapsed 0 43 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1508 with usage 1.06758 . Total units tried = 209\n",
      "try to drop unit 1508 from 0 th HD out of 49 possible.  usage down to 1.0675803647761708\n",
      "all done trying to reduce usage of unit 1508 final usage = 1.06758 255 sec elapsed 0 27 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1634 with usage 1.06722 . Total units tried = 210\n",
      "try to drop unit 1634 from 0 th HD out of 50 possible.  usage down to 1.0672195851871071\n",
      "all done trying to reduce usage of unit 1634 final usage = 1.06722 255 sec elapsed 0 14 successful, failed patches, couldn't start= 26\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1153 with usage 1.06718 . Total units tried = 211\n",
      "try to drop unit 1153 from 0 th HD out of 88 possible.  usage down to 1.0671776340721784\n",
      "all done trying to reduce usage of unit 1153 final usage = 1.06718 258 sec elapsed 0 42 successful, failed patches, couldn't start= 29\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 441 with usage 1.06708 . Total units tried = 212\n",
      "try to drop unit 441 from 0 th HD out of 86 possible.  usage down to 1.0670769513962184\n",
      "all done trying to reduce usage of unit 441 final usage = 1.06708 258 sec elapsed 0 54 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1126 with usage 1.06674 . Total units tried = 213\n",
      "try to drop unit 1126 from 0 th HD out of 86 possible.  usage down to 1.0667413424761487\n",
      "all done trying to reduce usage of unit 1126 final usage = 1.06674 262 sec elapsed 0 49 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 1594 with usage 1.06656 . Total units tried = 214\n",
      "try to drop unit 1594 from 0 th HD out of 70 possible.  usage down to 1.0665567575701038\n",
      "all done trying to reduce usage of unit 1594 final usage = 1.06656 263 sec elapsed 0 50 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99828 0.08666\n",
      "starting to reduce usage for unit 608 with usage 1.0658 . Total units tried = 215\n",
      "try to drop unit 608 from 0 th HD out of 84 possible.  usage down to 1.0658016375001362\n",
      "all done trying to reduce usage of unit 608 final usage = 1.04974 264 sec elapsed 1 67 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99827 0.08665\n",
      "starting to reduce usage for unit 1643 with usage 1.06566 . Total units tried = 216\n",
      "try to drop unit 1643 from 0 th HD out of 78 possible.  usage down to 1.0656590037090834\n",
      "all done trying to reduce usage of unit 1643 final usage = 1.06566 264 sec elapsed 0 51 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 0.99827 0.08665\n",
      "starting to reduce usage for unit 1615 with usage 1.06525 . Total units tried = 217\n",
      "try to drop unit 1615 from 0 th HD out of 70 possible.  usage down to 1.0652478827820684\n",
      "all done trying to reduce usage of unit 1615 final usage = 1.06525 265 sec elapsed 0 55 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99827 0.08665\n",
      "starting to reduce usage for unit 1127 with usage 1.06505 . Total units tried = 218\n",
      "try to drop unit 1127 from 0 th HD out of 90 possible.  usage down to 1.0650549076530957\n",
      "all done trying to reduce usage of unit 1127 final usage = 1.06505 268 sec elapsed 0 47 successful, failed patches, couldn't start= 25\n",
      "current avg and SD of usage are 0.99827 0.08665\n",
      "starting to reduce usage for unit 1100 with usage 1.065 . Total units tried = 219\n",
      "try to drop unit 1100 from 0 th HD out of 65 possible.  usage down to 1.0649961760921003\n",
      "all done trying to reduce usage of unit 1100 final usage = 1.065 270 sec elapsed 0 42 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.99827 0.08665\n",
      "starting to reduce usage for unit 327 with usage 1.0649 . Total units tried = 220\n",
      "try to drop unit 327 from 0 th HD out of 79 possible.  usage down to 1.064903883639085\n",
      "all done trying to reduce usage of unit 327 final usage = 1.00782 271 sec elapsed 4 6 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99825 0.08661\n",
      "starting to reduce usage for unit 184 with usage 1.06489 . Total units tried = 221\n",
      "try to drop unit 184 from 0 th HD out of 88 possible.  usage down to 1.0648871031930727\n",
      "all done trying to reduce usage of unit 184 final usage = 1.06489 271 sec elapsed 0 64 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99825 0.08661\n",
      "starting to reduce usage for unit 1443 with usage 1.06422 . Total units tried = 222\n",
      "try to drop unit 1443 from 0 th HD out of 60 possible.  usage down to 1.0642158853530357\n",
      "all done trying to reduce usage of unit 1443 final usage = 1.06422 272 sec elapsed 0 14 successful, failed patches, couldn't start= 34\n",
      "current avg and SD of usage are 0.99825 0.08661\n",
      "starting to reduce usage for unit 1502 with usage 1.06401 . Total units tried = 223\n",
      "try to drop unit 1502 from 0 th HD out of 70 possible.  usage down to 1.0640145200010038\n",
      "all done trying to reduce usage of unit 1502 final usage = 1.06401 272 sec elapsed 0 37 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 0.99825 0.08661\n",
      "starting to reduce usage for unit 141 with usage 1.06381 . Total units tried = 224\n",
      "try to drop unit 141 from 0 th HD out of 106 possible.  usage down to 1.0638131546492535\n",
      "all done trying to reduce usage of unit 141 final usage = 1.01206 273 sec elapsed 6 12 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1222 with usage 1.06203 . Total units tried = 225\n",
      "try to drop unit 1222 from 0 th HD out of 56 possible.  usage down to 1.0620260371499333\n",
      "all done trying to reduce usage of unit 1222 final usage = 1.06203 275 sec elapsed 0 43 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1548 with usage 1.06141 . Total units tried = 226\n",
      "try to drop unit 1548 from 0 th HD out of 34 possible.  usage down to 1.0614135508709763\n",
      "all done trying to reduce usage of unit 1548 final usage = 1.06141 275 sec elapsed 0 25 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1107 with usage 1.06132 . Total units tried = 227\n",
      "try to drop unit 1107 from 0 th HD out of 67 possible.  usage down to 1.061321258417989\n",
      "all done trying to reduce usage of unit 1107 final usage = 1.06132 277 sec elapsed 0 50 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1621 with usage 1.06115 . Total units tried = 228\n",
      "try to drop unit 1621 from 0 th HD out of 90 possible.  usage down to 1.0611534539579195\n",
      "all done trying to reduce usage of unit 1621 final usage = 1.06115 278 sec elapsed 0 52 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1583 with usage 1.06103 . Total units tried = 229\n",
      "try to drop unit 1583 from 0 th HD out of 71 possible.  usage down to 1.06102760061295\n",
      "all done trying to reduce usage of unit 1583 final usage = 1.06103 279 sec elapsed 0 29 successful, failed patches, couldn't start= 28\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1158 with usage 1.06055 . Total units tried = 230\n",
      "try to drop unit 1158 from 0 th HD out of 90 possible.  usage down to 1.0605493579019665\n",
      "all done trying to reduce usage of unit 1158 final usage = 1.06055 282 sec elapsed 0 46 successful, failed patches, couldn't start= 26\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1524 with usage 1.06009 . Total units tried = 231\n",
      "try to drop unit 1524 from 0 th HD out of 71 possible.  usage down to 1.0600878956369162\n",
      "all done trying to reduce usage of unit 1524 final usage = 1.06009 283 sec elapsed 0 46 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1102 with usage 1.05991 . Total units tried = 232\n",
      "try to drop unit 1102 from 0 th HD out of 83 possible.  usage down to 1.0599117009539596\n",
      "all done trying to reduce usage of unit 1102 final usage = 1.05991 285 sec elapsed 0 42 successful, failed patches, couldn't start= 25\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1566 with usage 1.05982 . Total units tried = 233\n",
      "try to drop unit 1566 from 0 th HD out of 70 possible.  usage down to 1.0598194085009198\n",
      "all done trying to reduce usage of unit 1566 final usage = 1.05982 285 sec elapsed 0 26 successful, failed patches, couldn't start= 30\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1152 with usage 1.05977 . Total units tried = 234\n",
      "try to drop unit 1152 from 0 th HD out of 88 possible.  usage down to 1.0597690671629456\n",
      "all done trying to reduce usage of unit 1152 final usage = 1.05977 288 sec elapsed 0 58 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 677 with usage 1.0595 . Total units tried = 235\n",
      "try to drop unit 677 from 0 th HD out of 84 possible.  usage down to 1.059500580026944\n",
      "all done trying to reduce usage of unit 677 final usage = 1.0595 290 sec elapsed 0 49 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1650 with usage 1.05931 . Total units tried = 236\n",
      "try to drop unit 1650 from 0 th HD out of 94 possible.  usage down to 1.0593076048978758\n",
      "all done trying to reduce usage of unit 1650 final usage = 1.05931 291 sec elapsed 0 57 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1505 with usage 1.05927 . Total units tried = 237\n",
      "try to drop unit 1505 from 0 th HD out of 73 possible.  usage down to 1.0592740440058799\n",
      "all done trying to reduce usage of unit 1505 final usage = 1.05927 291 sec elapsed 0 35 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1890 with usage 1.05863 . Total units tried = 238\n",
      "try to drop unit 1890 from 0 th HD out of 102 possible.  usage down to 1.0586279968349763\n",
      "all done trying to reduce usage of unit 1890 final usage = 1.05863 294 sec elapsed 0 63 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1647 with usage 1.05857 . Total units tried = 239\n",
      "try to drop unit 1647 from 0 th HD out of 102 possible.  usage down to 1.0585692652739014\n",
      "all done trying to reduce usage of unit 1647 final usage = 1.05857 294 sec elapsed 0 68 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1146 with usage 1.05844 . Total units tried = 240\n",
      "try to drop unit 1146 from 0 th HD out of 74 possible.  usage down to 1.058435021705801\n",
      "all done trying to reduce usage of unit 1146 final usage = 1.05844 296 sec elapsed 0 49 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1510 with usage 1.0576 . Total units tried = 241\n",
      "try to drop unit 1510 from 0 th HD out of 74 possible.  usage down to 1.0576043896288396\n",
      "all done trying to reduce usage of unit 1510 final usage = 1.0576 297 sec elapsed 0 28 successful, failed patches, couldn't start= 32\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1616 with usage 1.05747 . Total units tried = 242\n",
      "try to drop unit 1616 from 0 th HD out of 59 possible.  usage down to 1.0574701460608245\n",
      "all done trying to reduce usage of unit 1616 final usage = 1.05747 298 sec elapsed 0 48 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1639 with usage 1.05731 . Total units tried = 243\n",
      "try to drop unit 1639 from 0 th HD out of 90 possible.  usage down to 1.057310731823799\n",
      "all done trying to reduce usage of unit 1639 final usage = 1.05731 299 sec elapsed 0 45 successful, failed patches, couldn't start= 27\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1154 with usage 1.05718 . Total units tried = 244\n",
      "try to drop unit 1154 from 0 th HD out of 58 possible.  usage down to 1.0571764882558266\n",
      "all done trying to reduce usage of unit 1154 final usage = 1.05718 301 sec elapsed 0 40 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1496 with usage 1.05712 . Total units tried = 245\n",
      "try to drop unit 1496 from 0 th HD out of 60 possible.  usage down to 1.0571177566948016\n",
      "all done trying to reduce usage of unit 1496 final usage = 1.05712 302 sec elapsed 0 36 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1558 with usage 1.05661 . Total units tried = 246\n",
      "try to drop unit 1558 from 0 th HD out of 89 possible.  usage down to 1.0566059530918028\n",
      "all done trying to reduce usage of unit 1558 final usage = 1.05661 302 sec elapsed 0 48 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 215 with usage 1.05616 . Total units tried = 247\n",
      "try to drop unit 215 from 0 th HD out of 85 possible.  usage down to 1.0561612712727846\n",
      "all done trying to reduce usage of unit 215 final usage = 1.05616 303 sec elapsed 0 65 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1573 with usage 1.05577 . Total units tried = 248\n",
      "try to drop unit 1573 from 0 th HD out of 67 possible.  usage down to 1.0557669307917499\n",
      "all done trying to reduce usage of unit 1573 final usage = 1.05577 304 sec elapsed 0 49 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1624 with usage 1.05569 . Total units tried = 249\n",
      "try to drop unit 1624 from 0 th HD out of 95 possible.  usage down to 1.0556914187847681\n",
      "all done trying to reduce usage of unit 1624 final usage = 1.05569 305 sec elapsed 0 44 successful, failed patches, couldn't start= 32\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 1641 with usage 1.05567 . Total units tried = 250\n",
      "try to drop unit 1641 from 0 th HD out of 78 possible.  usage down to 1.055674638338772\n",
      "all done trying to reduce usage of unit 1641 final usage = 1.05567 306 sec elapsed 0 57 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99827 0.08659\n",
      "starting to reduce usage for unit 719 with usage 1.05553 . Total units tried = 251\n",
      "try to drop unit 719 from 0 th HD out of 52 possible.  usage down to 1.055532004547822\n",
      "all done trying to reduce usage of unit 719 final usage = 1.04194 307 sec elapsed 1 33 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99827 0.08638\n",
      "starting to reduce usage for unit 29 with usage 1.05481 . Total units tried = 252\n",
      "try to drop unit 29 from 0 th HD out of 81 possible.  usage down to 1.0548104453697535\n",
      "all done trying to reduce usage of unit 29 final usage = 1.00561 307 sec elapsed 5 34 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99827 0.08636\n",
      "starting to reduce usage for unit 1656 with usage 1.05452 . Total units tried = 253\n",
      "try to drop unit 1656 from 0 th HD out of 131 possible.  usage down to 1.054516787564984\n",
      "try to drop unit 1656 from 90 th HD out of 131 possible.  usage down to 1.054516787564984\n",
      "all done trying to reduce usage of unit 1656 final usage = 1.05452 313 sec elapsed 0 102 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99827 0.08636\n",
      "starting to reduce usage for unit 1525 with usage 1.05439 . Total units tried = 254\n",
      "try to drop unit 1525 from 0 th HD out of 41 possible.  usage down to 1.0543909342197473\n",
      "all done trying to reduce usage of unit 1525 final usage = 1.05439 314 sec elapsed 0 22 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99827 0.08636\n",
      "starting to reduce usage for unit 179 with usage 1.0543 . Total units tried = 255\n",
      "try to drop unit 179 from 0 th HD out of 103 possible.  usage down to 1.0542986417669515\n",
      "all done trying to reduce usage of unit 179 final usage = 1.01091 314 sec elapsed 4 16 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99827 0.08633\n",
      "starting to reduce usage for unit 492 with usage 1.05376 . Total units tried = 256\n",
      "try to drop unit 492 from 0 th HD out of 78 possible.  usage down to 1.0537616674947319\n",
      "all done trying to reduce usage of unit 492 final usage = 1.00822 314 sec elapsed 4 10 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99827 0.0863\n",
      "starting to reduce usage for unit 233 with usage 1.05369 . Total units tried = 257\n",
      "try to drop unit 233 from 0 th HD out of 79 possible.  usage down to 1.053694545710724\n",
      "all done trying to reduce usage of unit 233 final usage = 1.05369 315 sec elapsed 0 56 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99827 0.0863\n",
      "starting to reduce usage for unit 1539 with usage 1.05366 . Total units tried = 258\n",
      "try to drop unit 1539 from 0 th HD out of 52 possible.  usage down to 1.0536609848186904\n",
      "all done trying to reduce usage of unit 1539 final usage = 1.05366 316 sec elapsed 0 41 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99827 0.0863\n",
      "starting to reduce usage for unit 39 with usage 1.05354 . Total units tried = 259\n",
      "try to drop unit 39 from 0 th HD out of 78 possible.  usage down to 1.053535131473717\n",
      "all done trying to reduce usage of unit 39 final usage = 1.01766 316 sec elapsed 4 23 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99825 0.08628\n",
      "starting to reduce usage for unit 1162 with usage 1.05307 . Total units tried = 260\n",
      "try to drop unit 1162 from 0 th HD out of 59 possible.  usage down to 1.053065278985656\n",
      "all done trying to reduce usage of unit 1162 final usage = 1.05307 318 sec elapsed 0 39 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99825 0.08628\n",
      "starting to reduce usage for unit 1498 with usage 1.05275 . Total units tried = 261\n",
      "try to drop unit 1498 from 0 th HD out of 55 possible.  usage down to 1.0527548407346699\n",
      "all done trying to reduce usage of unit 1498 final usage = 1.05275 319 sec elapsed 0 36 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99825 0.08628\n",
      "starting to reduce usage for unit 1622 with usage 1.05164 . Total units tried = 262\n",
      "try to drop unit 1622 from 0 th HD out of 86 possible.  usage down to 1.0516389410756306\n",
      "all done trying to reduce usage of unit 1622 final usage = 1.05164 321 sec elapsed 0 57 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 0.99825 0.08628\n",
      "starting to reduce usage for unit 1060 with usage 1.05162 . Total units tried = 263\n",
      "try to drop unit 1060 from 0 th HD out of 67 possible.  usage down to 1.0516221606297405\n",
      "all done trying to reduce usage of unit 1060 final usage = 1.01727 322 sec elapsed 3 17 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99825 0.08627\n",
      "starting to reduce usage for unit 1509 with usage 1.05133 . Total units tried = 264\n",
      "try to drop unit 1509 from 0 th HD out of 53 possible.  usage down to 1.051328502824632\n",
      "all done trying to reduce usage of unit 1509 final usage = 1.05133 323 sec elapsed 0 38 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99825 0.08627\n",
      "starting to reduce usage for unit 1653 with usage 1.05094 . Total units tried = 265\n",
      "try to drop unit 1653 from 0 th HD out of 101 possible.  usage down to 1.0509425525666691\n",
      "all done trying to reduce usage of unit 1653 final usage = 1.05094 323 sec elapsed 0 50 successful, failed patches, couldn't start= 31\n",
      "current avg and SD of usage are 0.99825 0.08627\n",
      "starting to reduce usage for unit 1894 with usage 1.05077 . Total units tried = 266\n",
      "try to drop unit 1894 from 0 th HD out of 126 possible.  usage down to 1.050774748106841\n",
      "all done trying to reduce usage of unit 1894 final usage = 1.01692 326 sec elapsed 4 64 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99825 0.08622\n",
      "starting to reduce usage for unit 1601 with usage 1.05031 . Total units tried = 267\n",
      "try to drop unit 1601 from 0 th HD out of 69 possible.  usage down to 1.0503132858415936\n",
      "all done trying to reduce usage of unit 1601 final usage = 1.05031 327 sec elapsed 0 54 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99825 0.08622\n",
      "starting to reduce usage for unit 388 with usage 1.05024 . Total units tried = 268\n",
      "try to drop unit 388 from 0 th HD out of 99 possible.  usage down to 1.0502377738346635\n",
      "all done trying to reduce usage of unit 388 final usage = 1.01205 327 sec elapsed 3 18 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99825 0.0862\n",
      "starting to reduce usage for unit 1541 with usage 1.04957 . Total units tried = 269\n",
      "try to drop unit 1541 from 0 th HD out of 74 possible.  usage down to 1.0495749462175374\n",
      "all done trying to reduce usage of unit 1541 final usage = 1.04957 328 sec elapsed 0 30 successful, failed patches, couldn't start= 30\n",
      "current avg and SD of usage are 0.99825 0.0862\n",
      "starting to reduce usage for unit 1195 with usage 1.04903 . Total units tried = 270\n",
      "try to drop unit 1195 from 0 th HD out of 54 possible.  usage down to 1.0490295817224857\n",
      "all done trying to reduce usage of unit 1195 final usage = 1.04903 329 sec elapsed 0 41 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99825 0.0862\n",
      "starting to reduce usage for unit 1952 with usage 1.0483 . Total units tried = 271\n",
      "try to drop unit 1952 from 0 th HD out of 114 possible.  usage down to 1.0482996323216887\n",
      "try to drop unit 1952 from 90 th HD out of 114 possible.  usage down to 1.0482996323216887\n",
      "all done trying to reduce usage of unit 1952 final usage = 1.0483 332 sec elapsed 0 65 successful, failed patches, couldn't start= 27\n",
      "current avg and SD of usage are 0.99825 0.0862\n",
      "starting to reduce usage for unit 1531 with usage 1.0482 . Total units tried = 272\n",
      "try to drop unit 1531 from 0 th HD out of 73 possible.  usage down to 1.0481989496455746\n",
      "all done trying to reduce usage of unit 1531 final usage = 1.0482 333 sec elapsed 0 52 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99825 0.0862\n",
      "starting to reduce usage for unit 1545 with usage 1.04817 . Total units tried = 273\n",
      "try to drop unit 1545 from 0 th HD out of 74 possible.  usage down to 1.048165388753541\n",
      "all done trying to reduce usage of unit 1545 final usage = 1.03898 334 sec elapsed 1 24 successful, failed patches, couldn't start= 35\n",
      "current avg and SD of usage are 0.99825 0.0862\n",
      "starting to reduce usage for unit 1874 with usage 1.04798 . Total units tried = 274\n",
      "try to drop unit 1874 from 0 th HD out of 104 possible.  usage down to 1.0479808038476688\n",
      "all done trying to reduce usage of unit 1874 final usage = 1.04798 339 sec elapsed 0 72 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 0.99825 0.0862\n",
      "starting to reduce usage for unit 1550 with usage 1.04768 . Total units tried = 275\n",
      "try to drop unit 1550 from 0 th HD out of 73 possible.  usage down to 1.0476787558195237\n",
      "all done trying to reduce usage of unit 1550 final usage = 1.04768 339 sec elapsed 0 26 successful, failed patches, couldn't start= 33\n",
      "current avg and SD of usage are 0.99825 0.0862\n",
      "starting to reduce usage for unit 1536 with usage 1.04711 . Total units tried = 276\n",
      "try to drop unit 1536 from 0 th HD out of 88 possible.  usage down to 1.0471082206554956\n",
      "all done trying to reduce usage of unit 1536 final usage = 1.04711 341 sec elapsed 0 63 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99825 0.0862\n",
      "starting to reduce usage for unit 289 with usage 1.04655 . Total units tried = 277\n",
      "try to drop unit 289 from 0 th HD out of 73 possible.  usage down to 1.0465460757144618\n",
      "all done trying to reduce usage of unit 289 final usage = 1.0181 341 sec elapsed 2 13 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99825 0.0862\n",
      "starting to reduce usage for unit 1155 with usage 1.04589 . Total units tried = 278\n",
      "try to drop unit 1155 from 0 th HD out of 89 possible.  usage down to 1.0458916383204953\n",
      "all done trying to reduce usage of unit 1155 final usage = 1.04589 344 sec elapsed 0 40 successful, failed patches, couldn't start= 32\n",
      "current avg and SD of usage are 0.99825 0.0862\n",
      "starting to reduce usage for unit 1528 with usage 1.04555 . Total units tried = 279\n",
      "try to drop unit 1528 from 0 th HD out of 72 possible.  usage down to 1.0455476391774916\n",
      "all done trying to reduce usage of unit 1528 final usage = 1.04555 344 sec elapsed 0 47 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99825 0.0862\n",
      "starting to reduce usage for unit 498 with usage 1.04551 . Total units tried = 280\n",
      "try to drop unit 498 from 0 th HD out of 108 possible.  usage down to 1.0455056880626816\n",
      "all done trying to reduce usage of unit 498 final usage = 1.04551 345 sec elapsed 0 80 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99825 0.0862\n",
      "starting to reduce usage for unit 646 with usage 1.04546 . Total units tried = 281\n",
      "try to drop unit 646 from 0 th HD out of 63 possible.  usage down to 1.0454553467245082\n",
      "all done trying to reduce usage of unit 646 final usage = 1.03304 346 sec elapsed 1 47 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99825 0.08601\n",
      "starting to reduce usage for unit 1402 with usage 1.04475 . Total units tried = 282\n",
      "try to drop unit 1402 from 0 th HD out of 81 possible.  usage down to 1.044750567992424\n",
      "all done trying to reduce usage of unit 1402 final usage = 1.04475 347 sec elapsed 0 50 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.99825 0.08601\n",
      "starting to reduce usage for unit 1506 with usage 1.04473 . Total units tried = 283\n",
      "try to drop unit 1506 from 0 th HD out of 71 possible.  usage down to 1.0447337875464173\n",
      "all done trying to reduce usage of unit 1506 final usage = 1.04473 348 sec elapsed 0 55 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99825 0.08601\n",
      "starting to reduce usage for unit 389 with usage 1.04473 . Total units tried = 284\n",
      "try to drop unit 389 from 0 th HD out of 59 possible.  usage down to 1.044725397323559\n",
      "all done trying to reduce usage of unit 389 final usage = 1.03198 348 sec elapsed 1 20 successful, failed patches, couldn't start= 27\n",
      "current avg and SD of usage are 0.99826 0.08598\n",
      "starting to reduce usage for unit 451 with usage 1.0445 . Total units tried = 285\n",
      "try to drop unit 451 from 0 th HD out of 103 possible.  usage down to 1.0444988613025776\n",
      "all done trying to reduce usage of unit 451 final usage = 1.01673 348 sec elapsed 3 26 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99826 0.08597\n",
      "starting to reduce usage for unit 1156 with usage 1.04372 . Total units tried = 286\n",
      "try to drop unit 1156 from 0 th HD out of 63 possible.  usage down to 1.0437185705633922\n",
      "all done trying to reduce usage of unit 1156 final usage = 1.04372 351 sec elapsed 0 43 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99826 0.08597\n",
      "starting to reduce usage for unit 369 with usage 1.0435 . Total units tried = 287\n",
      "try to drop unit 369 from 0 th HD out of 68 possible.  usage down to 1.0435004247653987\n",
      "all done trying to reduce usage of unit 369 final usage = 1.01336 351 sec elapsed 2 10 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99826 0.08594\n",
      "starting to reduce usage for unit 1572 with usage 1.04345 . Total units tried = 288\n",
      "try to drop unit 1572 from 0 th HD out of 95 possible.  usage down to 1.043450083427402\n",
      "all done trying to reduce usage of unit 1572 final usage = 1.02248 352 sec elapsed 2 65 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99825 0.08594\n",
      "starting to reduce usage for unit 306 with usage 1.04343 . Total units tried = 289\n",
      "try to drop unit 306 from 0 th HD out of 77 possible.  usage down to 1.0434333029813878\n",
      "all done trying to reduce usage of unit 306 final usage = 1.01228 353 sec elapsed 3 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99824 0.08593\n",
      "starting to reduce usage for unit 723 with usage 1.04336 . Total units tried = 290\n",
      "try to drop unit 723 from 0 th HD out of 69 possible.  usage down to 1.0433577909744693\n",
      "all done trying to reduce usage of unit 723 final usage = 1.04336 354 sec elapsed 0 49 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99824 0.08593\n",
      "starting to reduce usage for unit 297 with usage 1.04327 . Total units tried = 291\n",
      "try to drop unit 297 from 0 th HD out of 52 possible.  usage down to 1.0432738887443522\n",
      "all done trying to reduce usage of unit 297 final usage = 1.01384 354 sec elapsed 2 8 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99823 0.08592\n",
      "starting to reduce usage for unit 643 with usage 1.04311 . Total units tried = 292\n",
      "try to drop unit 643 from 0 th HD out of 93 possible.  usage down to 1.0431144745074354\n",
      "all done trying to reduce usage of unit 643 final usage = 1.04311 356 sec elapsed 0 54 successful, failed patches, couldn't start= 21\n",
      "current avg and SD of usage are 0.99823 0.08592\n",
      "starting to reduce usage for unit 1648 with usage 1.04306 . Total units tried = 293\n",
      "try to drop unit 1648 from 0 th HD out of 91 possible.  usage down to 1.043055742946394\n",
      "all done trying to reduce usage of unit 1648 final usage = 1.04306 357 sec elapsed 0 42 successful, failed patches, couldn't start= 31\n",
      "current avg and SD of usage are 0.99823 0.08592\n",
      "starting to reduce usage for unit 1575 with usage 1.04263 . Total units tried = 294\n",
      "try to drop unit 1575 from 0 th HD out of 58 possible.  usage down to 1.0426278415733181\n",
      "all done trying to reduce usage of unit 1575 final usage = 1.04263 358 sec elapsed 0 41 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99823 0.08592\n",
      "starting to reduce usage for unit 1441 with usage 1.04172 . Total units tried = 295\n",
      "try to drop unit 1441 from 0 th HD out of 64 possible.  usage down to 1.0417216974893388\n",
      "all done trying to reduce usage of unit 1441 final usage = 1.04172 359 sec elapsed 0 24 successful, failed patches, couldn't start= 28\n",
      "current avg and SD of usage are 0.99823 0.08592\n",
      "starting to reduce usage for unit 1562 with usage 1.0417 . Total units tried = 296\n",
      "try to drop unit 1562 from 0 th HD out of 82 possible.  usage down to 1.0417049170433679\n",
      "all done trying to reduce usage of unit 1562 final usage = 1.0417 360 sec elapsed 0 54 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 0.99823 0.08592\n",
      "starting to reduce usage for unit 1444 with usage 1.04113 . Total units tried = 297\n",
      "try to drop unit 1444 from 0 th HD out of 67 possible.  usage down to 1.0411343818793257\n",
      "all done trying to reduce usage of unit 1444 final usage = 1.04113 360 sec elapsed 0 25 successful, failed patches, couldn't start= 29\n",
      "current avg and SD of usage are 0.99823 0.08592\n",
      "starting to reduce usage for unit 1638 with usage 1.04106 . Total units tried = 298\n",
      "try to drop unit 1638 from 0 th HD out of 74 possible.  usage down to 1.0410588698723042\n",
      "all done trying to reduce usage of unit 1638 final usage = 1.04106 361 sec elapsed 0 40 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 0.99823 0.08592\n",
      "starting to reduce usage for unit 187 with usage 1.04104 . Total units tried = 299\n",
      "try to drop unit 187 from 0 th HD out of 85 possible.  usage down to 1.0410420894264483\n",
      "all done trying to reduce usage of unit 187 final usage = 1.01356 361 sec elapsed 4 30 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99822 0.0859\n",
      "starting to reduce usage for unit 1903 with usage 1.04043 . Total units tried = 300\n",
      "try to drop unit 1903 from 0 th HD out of 61 possible.  usage down to 1.040429603147202\n",
      "all done trying to reduce usage of unit 1903 final usage = 1.00611 365 sec elapsed 3 33 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99821 0.08589\n",
      "starting to reduce usage for unit 167 with usage 1.04018 . Total units tried = 301\n",
      "try to drop unit 167 from 0 th HD out of 82 possible.  usage down to 1.040177896457313\n",
      "all done trying to reduce usage of unit 167 final usage = 1.01406 365 sec elapsed 2 13 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99823 0.0857\n",
      "starting to reduce usage for unit 1848 with usage 1.03991 . Total units tried = 302\n",
      "try to drop unit 1848 from 0 th HD out of 119 possible.  usage down to 1.0399094093215253\n",
      "try to drop unit 1848 from 90 th HD out of 119 possible.  usage down to 1.0399094093215253\n",
      "all done trying to reduce usage of unit 1848 final usage = 1.03991 368 sec elapsed 0 69 successful, failed patches, couldn't start= 27\n",
      "current avg and SD of usage are 0.99823 0.0857\n",
      "starting to reduce usage for unit 1117 with usage 1.03991 . Total units tried = 303\n",
      "try to drop unit 1117 from 0 th HD out of 66 possible.  usage down to 1.0399094093212564\n",
      "all done trying to reduce usage of unit 1117 final usage = 1.03991 370 sec elapsed 0 45 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99823 0.0857\n",
      "starting to reduce usage for unit 1308 with usage 1.0396 . Total units tried = 304\n",
      "try to drop unit 1308 from 0 th HD out of 82 possible.  usage down to 1.039598971070266\n",
      "all done trying to reduce usage of unit 1308 final usage = 1.01198 371 sec elapsed 3 40 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.99822 0.0857\n",
      "starting to reduce usage for unit 209 with usage 1.03929 . Total units tried = 305\n",
      "try to drop unit 209 from 0 th HD out of 80 possible.  usage down to 1.0392885328192774\n",
      "all done trying to reduce usage of unit 209 final usage = 1.02011 371 sec elapsed 2 52 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.99821 0.08569\n",
      "starting to reduce usage for unit 504 with usage 1.03831 . Total units tried = 306\n",
      "try to drop unit 504 from 0 th HD out of 82 possible.  usage down to 1.0383068767282502\n",
      "all done trying to reduce usage of unit 504 final usage = 1.03831 371 sec elapsed 0 55 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99821 0.08569\n",
      "starting to reduce usage for unit 1598 with usage 1.03825 . Total units tried = 307\n",
      "try to drop unit 1598 from 0 th HD out of 75 possible.  usage down to 1.0382481451672252\n",
      "all done trying to reduce usage of unit 1598 final usage = 1.03825 372 sec elapsed 0 50 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.99821 0.08569\n",
      "starting to reduce usage for unit 1546 with usage 1.03823 . Total units tried = 308\n",
      "try to drop unit 1546 from 0 th HD out of 69 possible.  usage down to 1.0382313647212185\n",
      "all done trying to reduce usage of unit 1546 final usage = 1.03823 373 sec elapsed 0 54 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99821 0.08569\n",
      "starting to reduce usage for unit 1121 with usage 1.0378 . Total units tried = 309\n",
      "try to drop unit 1121 from 0 th HD out of 58 possible.  usage down to 1.03780346334822\n",
      "all done trying to reduce usage of unit 1121 final usage = 1.0378 375 sec elapsed 0 37 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.99821 0.08569\n",
      "starting to reduce usage for unit 1567 with usage 1.0378 . Total units tried = 310\n",
      "try to drop unit 1567 from 0 th HD out of 73 possible.  usage down to 1.0377950731252326\n",
      "all done trying to reduce usage of unit 1567 final usage = 1.0378 375 sec elapsed 0 49 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.99821 0.08569\n",
      "starting to reduce usage for unit 1135 with usage 1.03757 . Total units tried = 311\n",
      "try to drop unit 1135 from 0 th HD out of 67 possible.  usage down to 1.0375685371041952\n",
      "all done trying to reduce usage of unit 1135 final usage = 1.03757 378 sec elapsed 0 46 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99821 0.08569\n",
      "starting to reduce usage for unit 1400 with usage 1.03755 . Total units tried = 312\n",
      "try to drop unit 1400 from 0 th HD out of 76 possible.  usage down to 1.0375517566581747\n",
      "all done trying to reduce usage of unit 1400 final usage = 1.01039 378 sec elapsed 2 22 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99821 0.08568\n",
      "starting to reduce usage for unit 1175 with usage 1.03745 . Total units tried = 313\n",
      "try to drop unit 1175 from 0 th HD out of 72 possible.  usage down to 1.0374510739822043\n",
      "all done trying to reduce usage of unit 1175 final usage = 1.03745 380 sec elapsed 0 37 successful, failed patches, couldn't start= 21\n",
      "current avg and SD of usage are 0.99821 0.08568\n",
      "starting to reduce usage for unit 1571 with usage 1.03721 . Total units tried = 314\n",
      "try to drop unit 1571 from 0 th HD out of 90 possible.  usage down to 1.0372077575151868\n",
      "all done trying to reduce usage of unit 1571 final usage = 1.03721 381 sec elapsed 0 40 successful, failed patches, couldn't start= 32\n",
      "current avg and SD of usage are 0.99821 0.08568\n",
      "starting to reduce usage for unit 1706 with usage 1.03662 . Total units tried = 315\n",
      "try to drop unit 1706 from 0 th HD out of 59 possible.  usage down to 1.036620441905449\n",
      "all done trying to reduce usage of unit 1706 final usage = 1.02633 385 sec elapsed 1 37 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.99821 0.08568\n",
      "starting to reduce usage for unit 1392 with usage 1.03634 . Total units tried = 316\n",
      "try to drop unit 1392 from 0 th HD out of 44 possible.  usage down to 1.036335174323097\n",
      "all done trying to reduce usage of unit 1392 final usage = 1.03634 387 sec elapsed 0 33 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99821 0.08568\n",
      "starting to reduce usage for unit 1258 with usage 1.0355 . Total units tried = 317\n",
      "try to drop unit 1258 from 0 th HD out of 60 possible.  usage down to 1.035504542246172\n",
      "all done trying to reduce usage of unit 1258 final usage = 1.02608 388 sec elapsed 1 33 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.9982 0.08567\n",
      "starting to reduce usage for unit 1482 with usage 1.03537 . Total units tried = 318\n",
      "try to drop unit 1482 from 0 th HD out of 61 possible.  usage down to 1.0353702986781748\n",
      "all done trying to reduce usage of unit 1482 final usage = 1.03537 389 sec elapsed 0 38 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.9982 0.08567\n",
      "starting to reduce usage for unit 48 with usage 1.03507 . Total units tried = 319\n",
      "try to drop unit 48 from 0 th HD out of 70 possible.  usage down to 1.0350682506500413\n",
      "all done trying to reduce usage of unit 48 final usage = 1.00711 390 sec elapsed 2 20 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99821 0.08567\n",
      "starting to reduce usage for unit 1140 with usage 1.0347 . Total units tried = 320\n",
      "try to drop unit 1140 from 0 th HD out of 67 possible.  usage down to 1.0346990808380696\n",
      "all done trying to reduce usage of unit 1140 final usage = 1.0347 392 sec elapsed 0 50 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99821 0.08567\n",
      "starting to reduce usage for unit 1136 with usage 1.03462 . Total units tried = 321\n",
      "try to drop unit 1136 from 0 th HD out of 72 possible.  usage down to 1.0346151786080893\n",
      "all done trying to reduce usage of unit 1136 final usage = 1.03462 394 sec elapsed 0 50 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99821 0.08567\n",
      "starting to reduce usage for unit 1253 with usage 1.03456 . Total units tried = 322\n",
      "try to drop unit 1253 from 0 th HD out of 60 possible.  usage down to 1.0345564470470867\n",
      "all done trying to reduce usage of unit 1253 final usage = 1.03456 396 sec elapsed 0 44 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99821 0.08567\n",
      "starting to reduce usage for unit 1420 with usage 1.03401 . Total units tried = 323\n",
      "try to drop unit 1420 from 0 th HD out of 77 possible.  usage down to 1.034011082552077\n",
      "all done trying to reduce usage of unit 1420 final usage = 1.0097 397 sec elapsed 1 6 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99822 0.08562\n",
      "starting to reduce usage for unit 1492 with usage 1.03378 . Total units tried = 324\n",
      "try to drop unit 1492 from 0 th HD out of 70 possible.  usage down to 1.0337845465311033\n",
      "all done trying to reduce usage of unit 1492 final usage = 1.03378 398 sec elapsed 0 49 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99822 0.08562\n",
      "starting to reduce usage for unit 1214 with usage 1.03361 . Total units tried = 325\n",
      "try to drop unit 1214 from 0 th HD out of 70 possible.  usage down to 1.0336083518481027\n",
      "all done trying to reduce usage of unit 1214 final usage = 1.03361 400 sec elapsed 0 41 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.99822 0.08562\n",
      "starting to reduce usage for unit 1219 with usage 1.0336 . Total units tried = 326\n",
      "try to drop unit 1219 from 0 th HD out of 63 possible.  usage down to 1.0335999616250733\n",
      "all done trying to reduce usage of unit 1219 final usage = 1.0336 401 sec elapsed 0 42 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99822 0.08562\n",
      "starting to reduce usage for unit 1373 with usage 1.03337 . Total units tried = 327\n",
      "try to drop unit 1373 from 0 th HD out of 74 possible.  usage down to 1.0333734256040361\n",
      "all done trying to reduce usage of unit 1373 final usage = 1.0097 402 sec elapsed 2 32 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 0.9982 0.08562\n",
      "starting to reduce usage for unit 1116 with usage 1.03324 . Total units tried = 328\n",
      "try to drop unit 1116 from 0 th HD out of 57 possible.  usage down to 1.0332391820360696\n",
      "all done trying to reduce usage of unit 1116 final usage = 1.03324 404 sec elapsed 0 38 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.9982 0.08562\n",
      "starting to reduce usage for unit 1182 with usage 1.03266 . Total units tried = 329\n",
      "try to drop unit 1182 from 0 th HD out of 87 possible.  usage down to 1.0326602566491236\n",
      "all done trying to reduce usage of unit 1182 final usage = 1.03266 407 sec elapsed 0 64 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.9982 0.08562\n",
      "starting to reduce usage for unit 1487 with usage 1.03236 . Total units tried = 330\n",
      "try to drop unit 1487 from 0 th HD out of 67 possible.  usage down to 1.032358208621057\n",
      "all done trying to reduce usage of unit 1487 final usage = 1.03236 408 sec elapsed 0 37 successful, failed patches, couldn't start= 17\n",
      "current avg and SD of usage are 0.9982 0.08562\n",
      "starting to reduce usage for unit 1708 with usage 1.03222 . Total units tried = 331\n",
      "try to drop unit 1708 from 0 th HD out of 72 possible.  usage down to 1.03222396505329\n",
      "all done trying to reduce usage of unit 1708 final usage = 1.02427 412 sec elapsed 1 42 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.9982 0.08561\n",
      "starting to reduce usage for unit 12 with usage 1.03218 . Total units tried = 332\n",
      "try to drop unit 12 from 0 th HD out of 79 possible.  usage down to 1.0321820139380922\n",
      "all done trying to reduce usage of unit 12 final usage = 1.01456 412 sec elapsed 2 43 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.9982 0.0856\n",
      "starting to reduce usage for unit 1826 with usage 1.03194 . Total units tried = 333\n",
      "try to drop unit 1826 from 0 th HD out of 112 possible.  usage down to 1.0319386974712157\n",
      "all done trying to reduce usage of unit 1826 final usage = 1.00344 414 sec elapsed 2 18 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99819 0.0856\n",
      "starting to reduce usage for unit 1518 with usage 1.03177 . Total units tried = 334\n",
      "try to drop unit 1518 from 0 th HD out of 67 possible.  usage down to 1.0317708930110812\n",
      "all done trying to reduce usage of unit 1518 final usage = 1.03177 414 sec elapsed 0 36 successful, failed patches, couldn't start= 18\n",
      "current avg and SD of usage are 0.99819 0.0856\n",
      "starting to reduce usage for unit 404 with usage 1.03159 . Total units tried = 335\n",
      "try to drop unit 404 from 0 th HD out of 53 possible.  usage down to 1.0315946983280153\n",
      "all done trying to reduce usage of unit 404 final usage = 1.01091 415 sec elapsed 2 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99818 0.08558\n",
      "starting to reduce usage for unit 529 with usage 1.03082 . Total units tried = 336\n",
      "try to drop unit 529 from 0 th HD out of 65 possible.  usage down to 1.0308227978120033\n",
      "all done trying to reduce usage of unit 529 final usage = 1.03082 415 sec elapsed 0 32 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 0.99818 0.08558\n",
      "starting to reduce usage for unit 701 with usage 1.03059 . Total units tried = 337\n",
      "try to drop unit 701 from 0 th HD out of 75 possible.  usage down to 1.030587871568052\n",
      "all done trying to reduce usage of unit 701 final usage = 1.03059 417 sec elapsed 0 45 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.99818 0.08558\n",
      "starting to reduce usage for unit 135 with usage 1.03032 . Total units tried = 338\n",
      "try to drop unit 135 from 0 th HD out of 79 possible.  usage down to 1.0303193844319616\n",
      "all done trying to reduce usage of unit 135 final usage = 1.01658 417 sec elapsed 2 4 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99818 0.08557\n",
      "starting to reduce usage for unit 1213 with usage 1.03029 . Total units tried = 339\n",
      "try to drop unit 1213 from 0 th HD out of 73 possible.  usage down to 1.03028582353997\n",
      "all done trying to reduce usage of unit 1213 final usage = 1.03029 419 sec elapsed 0 37 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 0.99818 0.08557\n",
      "starting to reduce usage for unit 713 with usage 1.03023 . Total units tried = 340\n",
      "try to drop unit 713 from 0 th HD out of 53 possible.  usage down to 1.0302270919790317\n",
      "all done trying to reduce usage of unit 713 final usage = 1.03023 420 sec elapsed 0 34 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99818 0.08557\n",
      "starting to reduce usage for unit 1543 with usage 1.03019 . Total units tried = 341\n",
      "try to drop unit 1543 from 0 th HD out of 86 possible.  usage down to 1.0301935310869843\n",
      "all done trying to reduce usage of unit 1543 final usage = 1.03019 421 sec elapsed 0 29 successful, failed patches, couldn't start= 40\n",
      "current avg and SD of usage are 0.99818 0.08557\n",
      "starting to reduce usage for unit 1006 with usage 1.03 . Total units tried = 342\n",
      "try to drop unit 1006 from 0 th HD out of 69 possible.  usage down to 1.030000555957938\n",
      "all done trying to reduce usage of unit 1006 final usage = 1.01684 421 sec elapsed 1 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99818 0.08557\n",
      "starting to reduce usage for unit 1257 with usage 1.02972 . Total units tried = 343\n",
      "try to drop unit 1257 from 0 th HD out of 72 possible.  usage down to 1.0297236785989652\n",
      "all done trying to reduce usage of unit 1257 final usage = 1.02242 423 sec elapsed 1 30 successful, failed patches, couldn't start= 27\n",
      "current avg and SD of usage are 0.99818 0.08557\n",
      "starting to reduce usage for unit 1211 with usage 1.02955 . Total units tried = 344\n",
      "try to drop unit 1211 from 0 th HD out of 76 possible.  usage down to 1.0295474839159047\n",
      "all done trying to reduce usage of unit 1211 final usage = 1.02955 425 sec elapsed 0 38 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 0.99818 0.08557\n",
      "starting to reduce usage for unit 157 with usage 1.02927 . Total units tried = 345\n",
      "try to drop unit 157 from 0 th HD out of 68 possible.  usage down to 1.0292706065570023\n",
      "all done trying to reduce usage of unit 157 final usage = 1.00619 425 sec elapsed 2 5 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99817 0.08555\n",
      "starting to reduce usage for unit 1359 with usage 1.02923 . Total units tried = 346\n",
      "try to drop unit 1359 from 0 th HD out of 82 possible.  usage down to 1.0292286554419476\n",
      "all done trying to reduce usage of unit 1359 final usage = 1.02923 428 sec elapsed 0 60 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99817 0.08555\n",
      "starting to reduce usage for unit 409 with usage 1.02914 . Total units tried = 347\n",
      "try to drop unit 409 from 0 th HD out of 77 possible.  usage down to 1.029136362988949\n",
      "all done trying to reduce usage of unit 409 final usage = 1.01617 429 sec elapsed 1 33 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99817 0.08555\n",
      "starting to reduce usage for unit 1314 with usage 1.02894 . Total units tried = 348\n",
      "try to drop unit 1314 from 0 th HD out of 63 possible.  usage down to 1.0289433878599907\n",
      "all done trying to reduce usage of unit 1314 final usage = 1.02894 430 sec elapsed 0 50 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99817 0.08555\n",
      "starting to reduce usage for unit 1834 with usage 1.02893 . Total units tried = 349\n",
      "try to drop unit 1834 from 0 th HD out of 129 possible.  usage down to 1.0289266074142056\n",
      "all done trying to reduce usage of unit 1834 final usage = 1.01693 431 sec elapsed 1 5 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99817 0.0855\n",
      "starting to reduce usage for unit 74 with usage 1.02881 . Total units tried = 350\n",
      "try to drop unit 74 from 0 th HD out of 73 possible.  usage down to 1.0288091442920084\n",
      "all done trying to reduce usage of unit 74 final usage = 1.01398 432 sec elapsed 1 50 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99816 0.08549\n",
      "starting to reduce usage for unit 7 with usage 1.02862 . Total units tried = 351\n",
      "try to drop unit 7 from 0 th HD out of 108 possible.  usage down to 1.0286245593860897\n",
      "all done trying to reduce usage of unit 7 final usage = 1.01791 432 sec elapsed 2 12 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99816 0.08549\n",
      "starting to reduce usage for unit 1181 with usage 1.0286 . Total units tried = 352\n",
      "try to drop unit 1181 from 0 th HD out of 87 possible.  usage down to 1.0285993887169884\n",
      "all done trying to reduce usage of unit 1181 final usage = 1.0286 435 sec elapsed 0 67 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99816 0.08549\n",
      "starting to reduce usage for unit 560 with usage 1.02855 . Total units tried = 353\n",
      "try to drop unit 560 from 0 th HD out of 95 possible.  usage down to 1.0285490473791197\n",
      "all done trying to reduce usage of unit 560 final usage = 1.00982 435 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99816 0.08549\n",
      "starting to reduce usage for unit 728 with usage 1.02848 . Total units tried = 354\n",
      "try to drop unit 728 from 0 th HD out of 66 possible.  usage down to 1.02848192559497\n",
      "all done trying to reduce usage of unit 728 final usage = 1.02848 437 sec elapsed 0 47 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99816 0.08549\n",
      "starting to reduce usage for unit 1193 with usage 1.0283 . Total units tried = 355\n",
      "try to drop unit 1193 from 0 th HD out of 62 possible.  usage down to 1.028297340688929\n",
      "all done trying to reduce usage of unit 1193 final usage = 1.01692 437 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99814 0.08549\n",
      "starting to reduce usage for unit 816 with usage 1.02822 . Total units tried = 356\n",
      "try to drop unit 816 from 0 th HD out of 73 possible.  usage down to 1.0282218286819167\n",
      "all done trying to reduce usage of unit 816 final usage = 1.01687 437 sec elapsed 1 10 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99814 0.08548\n",
      "starting to reduce usage for unit 885 with usage 1.0282 . Total units tried = 357\n",
      "try to drop unit 885 from 0 th HD out of 76 possible.  usage down to 1.0281966580129116\n",
      "all done trying to reduce usage of unit 885 final usage = 1.01924 438 sec elapsed 1 24 successful, failed patches, couldn't start= 18\n",
      "current avg and SD of usage are 0.99814 0.08548\n",
      "starting to reduce usage for unit 1522 with usage 1.0282 . Total units tried = 358\n",
      "try to drop unit 1522 from 0 th HD out of 63 possible.  usage down to 1.0281966580129032\n",
      "all done trying to reduce usage of unit 1522 final usage = 1.0282 440 sec elapsed 0 37 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99814 0.08548\n",
      "starting to reduce usage for unit 977 with usage 1.02819 . Total units tried = 359\n",
      "try to drop unit 977 from 0 th HD out of 86 possible.  usage down to 1.028188267789914\n",
      "all done trying to reduce usage of unit 977 final usage = 1.01729 440 sec elapsed 1 4 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99813 0.08548\n",
      "starting to reduce usage for unit 1450 with usage 1.0279 . Total units tried = 360\n",
      "try to drop unit 1450 from 0 th HD out of 59 possible.  usage down to 1.0279030002079164\n",
      "all done trying to reduce usage of unit 1450 final usage = 1.0279 441 sec elapsed 0 16 successful, failed patches, couldn't start= 32\n",
      "current avg and SD of usage are 0.99813 0.08548\n",
      "starting to reduce usage for unit 1245 with usage 1.02779 . Total units tried = 361\n",
      "try to drop unit 1245 from 0 th HD out of 56 possible.  usage down to 1.0277855370858464\n",
      "all done trying to reduce usage of unit 1245 final usage = 1.02779 442 sec elapsed 0 23 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 0.99813 0.08548\n",
      "starting to reduce usage for unit 683 with usage 1.02776 . Total units tried = 362\n",
      "try to drop unit 683 from 0 th HD out of 59 possible.  usage down to 1.0277603664169066\n",
      "all done trying to reduce usage of unit 683 final usage = 1.02776 443 sec elapsed 0 47 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99813 0.08548\n",
      "starting to reduce usage for unit 590 with usage 1.02765 . Total units tried = 363\n",
      "try to drop unit 590 from 0 th HD out of 119 possible.  usage down to 1.0276512935182276\n",
      "all done trying to reduce usage of unit 590 final usage = 1.01514 444 sec elapsed 2 15 successful, failed patches, couldn't start= 38\n",
      "current avg and SD of usage are 0.99814 0.08547\n",
      "starting to reduce usage for unit 1864 with usage 1.02765 . Total units tried = 364\n",
      "try to drop unit 1864 from 0 th HD out of 110 possible.  usage down to 1.0276512935181268\n",
      "all done trying to reduce usage of unit 1864 final usage = 1.02765 446 sec elapsed 0 70 successful, failed patches, couldn't start= 18\n",
      "current avg and SD of usage are 0.99814 0.08547\n",
      "starting to reduce usage for unit 173 with usage 1.02747 . Total units tried = 365\n",
      "try to drop unit 173 from 0 th HD out of 84 possible.  usage down to 1.0274667086119678\n",
      "all done trying to reduce usage of unit 173 final usage = 1.00916 446 sec elapsed 1 4 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99813 0.08547\n",
      "starting to reduce usage for unit 884 with usage 1.02815 . Total units tried = 366\n",
      "try to drop unit 884 from 0 th HD out of 68 possible.  usage down to 1.0281463166749478\n",
      "all done trying to reduce usage of unit 884 final usage = 1.01017 448 sec elapsed 1 14 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99811 0.08547\n",
      "starting to reduce usage for unit 260 with usage 1.02778 . Total units tried = 367\n",
      "try to drop unit 260 from 0 th HD out of 91 possible.  usage down to 1.0277771468629926\n",
      "all done trying to reduce usage of unit 260 final usage = 1.0106 448 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99811 0.08547\n",
      "starting to reduce usage for unit 625 with usage 1.02747 . Total units tried = 368\n",
      "try to drop unit 625 from 0 th HD out of 79 possible.  usage down to 1.0274667086118905\n",
      "all done trying to reduce usage of unit 625 final usage = 1.02055 449 sec elapsed 1 53 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.99811 0.08547\n",
      "starting to reduce usage for unit 1891 with usage 1.02745 . Total units tried = 369\n",
      "try to drop unit 1891 from 0 th HD out of 94 possible.  usage down to 1.0274499281661351\n",
      "all done trying to reduce usage of unit 1891 final usage = 1.02745 452 sec elapsed 0 51 successful, failed patches, couldn't start= 25\n",
      "current avg and SD of usage are 0.99811 0.08547\n",
      "starting to reduce usage for unit 0 with usage 1.02741 . Total units tried = 370\n",
      "try to drop unit 0 from 0 th HD out of 60 possible.  usage down to 1.0274079770508413\n",
      "all done trying to reduce usage of unit 0 final usage = 1.01131 452 sec elapsed 1 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99811 0.08546\n",
      "starting to reduce usage for unit 1555 with usage 1.02728 . Total units tried = 371\n",
      "try to drop unit 1555 from 0 th HD out of 56 possible.  usage down to 1.0272821237059497\n",
      "all done trying to reduce usage of unit 1555 final usage = 1.02728 453 sec elapsed 0 42 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99811 0.08546\n",
      "starting to reduce usage for unit 75 with usage 1.02725 . Total units tried = 372\n",
      "try to drop unit 75 from 0 th HD out of 82 possible.  usage down to 1.0272485628139798\n",
      "all done trying to reduce usage of unit 75 final usage = 1.01121 453 sec elapsed 1 14 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.9981 0.08546\n",
      "starting to reduce usage for unit 1947 with usage 1.02724 . Total units tried = 373\n",
      "try to drop unit 1947 from 0 th HD out of 82 possible.  usage down to 1.0272401725910756\n",
      "all done trying to reduce usage of unit 1947 final usage = 1.02724 456 sec elapsed 0 42 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 0.9981 0.08546\n",
      "starting to reduce usage for unit 1681 with usage 1.02711 . Total units tried = 374\n",
      "try to drop unit 1681 from 0 th HD out of 65 possible.  usage down to 1.0271143192460894\n",
      "all done trying to reduce usage of unit 1681 final usage = 1.02069 458 sec elapsed 1 30 successful, failed patches, couldn't start= 21\n",
      "current avg and SD of usage are 0.9981 0.08545\n",
      "starting to reduce usage for unit 1422 with usage 1.02703 . Total units tried = 375\n",
      "try to drop unit 1422 from 0 th HD out of 78 possible.  usage down to 1.027030417015882\n",
      "all done trying to reduce usage of unit 1422 final usage = 1.01427 459 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9981 0.08545\n",
      "starting to reduce usage for unit 1579 with usage 1.02699 . Total units tried = 376\n",
      "try to drop unit 1579 from 0 th HD out of 91 possible.  usage down to 1.0269884659009\n",
      "all done trying to reduce usage of unit 1579 final usage = 1.01695 459 sec elapsed 1 34 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.9981 0.08545\n",
      "starting to reduce usage for unit 1911 with usage 1.02678 . Total units tried = 377\n",
      "try to drop unit 1911 from 0 th HD out of 92 possible.  usage down to 1.0267787103259247\n",
      "all done trying to reduce usage of unit 1911 final usage = 1.01551 461 sec elapsed 2 5 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99809 0.08544\n",
      "starting to reduce usage for unit 1179 with usage 1.02672 . Total units tried = 378\n",
      "try to drop unit 1179 from 0 th HD out of 62 possible.  usage down to 1.0267199787648738\n",
      "all done trying to reduce usage of unit 1179 final usage = 1.02672 462 sec elapsed 0 34 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 0.99809 0.08544\n",
      "starting to reduce usage for unit 336 with usage 1.02669 . Total units tried = 379\n",
      "try to drop unit 336 from 0 th HD out of 88 possible.  usage down to 1.0266948080958858\n",
      "all done trying to reduce usage of unit 336 final usage = 1.02669 463 sec elapsed 0 63 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99809 0.08544\n",
      "starting to reduce usage for unit 1376 with usage 1.02667 . Total units tried = 380\n",
      "try to drop unit 1376 from 0 th HD out of 78 possible.  usage down to 1.0266696374268391\n",
      "all done trying to reduce usage of unit 1376 final usage = 1.01123 463 sec elapsed 1 8 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.9981 0.08544\n",
      "starting to reduce usage for unit 827 with usage 1.02664 . Total units tried = 381\n",
      "try to drop unit 827 from 0 th HD out of 52 possible.  usage down to 1.0266444667578456\n",
      "all done trying to reduce usage of unit 827 final usage = 1.02664 464 sec elapsed 0 39 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.9981 0.08544\n",
      "starting to reduce usage for unit 1787 with usage 1.02659 . Total units tried = 382\n",
      "try to drop unit 1787 from 0 th HD out of 72 possible.  usage down to 1.0265857351967256\n",
      "all done trying to reduce usage of unit 1787 final usage = 1.01525 465 sec elapsed 1 13 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99809 0.08543\n",
      "starting to reduce usage for unit 128 with usage 1.02648 . Total units tried = 383\n",
      "try to drop unit 128 from 0 th HD out of 86 possible.  usage down to 1.0264766622977721\n",
      "all done trying to reduce usage of unit 128 final usage = 1.01064 465 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.9981 0.08544\n",
      "starting to reduce usage for unit 1178 with usage 1.02636 . Total units tried = 384\n",
      "try to drop unit 1178 from 0 th HD out of 88 possible.  usage down to 1.0263591991758605\n",
      "all done trying to reduce usage of unit 1178 final usage = 1.01221 466 sec elapsed 1 29 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 0.99809 0.08542\n",
      "starting to reduce usage for unit 502 with usage 1.02621 . Total units tried = 385\n",
      "try to drop unit 502 from 0 th HD out of 88 possible.  usage down to 1.0262081751619185\n",
      "all done trying to reduce usage of unit 502 final usage = 1.01413 467 sec elapsed 1 4 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99809 0.08542\n",
      "starting to reduce usage for unit 1633 with usage 1.02616 . Total units tried = 386\n",
      "try to drop unit 1633 from 0 th HD out of 42 possible.  usage down to 1.0261578338238388\n",
      "all done trying to reduce usage of unit 1633 final usage = 1.02616 467 sec elapsed 0 27 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99809 0.08542\n",
      "starting to reduce usage for unit 1252 with usage 1.02608 . Total units tried = 387\n",
      "try to drop unit 1252 from 0 th HD out of 54 possible.  usage down to 1.0260823218167974\n",
      "all done trying to reduce usage of unit 1252 final usage = 1.02608 468 sec elapsed 0 21 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 0.99809 0.08542\n",
      "starting to reduce usage for unit 1977 with usage 1.02607 . Total units tried = 388\n",
      "try to drop unit 1977 from 0 th HD out of 71 possible.  usage down to 1.0260655413708324\n",
      "all done trying to reduce usage of unit 1977 final usage = 1.01752 469 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99809 0.08542\n",
      "starting to reduce usage for unit 129 with usage 1.02597 . Total units tried = 389\n",
      "try to drop unit 129 from 0 th HD out of 87 possible.  usage down to 1.0259732489177962\n",
      "all done trying to reduce usage of unit 129 final usage = 1.01867 469 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99809 0.08541\n",
      "starting to reduce usage for unit 1374 with usage 1.02596 . Total units tried = 390\n",
      "try to drop unit 1374 from 0 th HD out of 77 possible.  usage down to 1.0259564684718259\n",
      "all done trying to reduce usage of unit 1374 final usage = 1.0048 470 sec elapsed 1 26 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.9981 0.08535\n",
      "starting to reduce usage for unit 909 with usage 1.02591 . Total units tried = 391\n",
      "try to drop unit 909 from 0 th HD out of 70 possible.  usage down to 1.0259145173568662\n",
      "all done trying to reduce usage of unit 909 final usage = 1.01558 470 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.9981 0.08535\n",
      "starting to reduce usage for unit 1526 with usage 1.02591 . Total units tried = 392\n",
      "try to drop unit 1526 from 0 th HD out of 57 possible.  usage down to 1.025906127133845\n",
      "all done trying to reduce usage of unit 1526 final usage = 1.01049 471 sec elapsed 1 35 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99809 0.08535\n",
      "starting to reduce usage for unit 720 with usage 1.0257 . Total units tried = 393\n",
      "try to drop unit 720 from 0 th HD out of 62 possible.  usage down to 1.0256963715589074\n",
      "all done trying to reduce usage of unit 720 final usage = 1.0257 472 sec elapsed 0 40 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.99809 0.08535\n",
      "starting to reduce usage for unit 427 with usage 1.02567 . Total units tried = 394\n",
      "try to drop unit 427 from 0 th HD out of 73 possible.  usage down to 1.025671200889823\n",
      "all done trying to reduce usage of unit 427 final usage = 1.01614 473 sec elapsed 1 15 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99809 0.08521\n",
      "starting to reduce usage for unit 1217 with usage 1.02547 . Total units tried = 395\n",
      "try to drop unit 1217 from 0 th HD out of 56 possible.  usage down to 1.02546983553781\n",
      "all done trying to reduce usage of unit 1217 final usage = 1.01132 475 sec elapsed 1 16 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99809 0.08521\n",
      "starting to reduce usage for unit 1151 with usage 1.02536 . Total units tried = 396\n",
      "try to drop unit 1151 from 0 th HD out of 77 possible.  usage down to 1.0253607626388208\n",
      "all done trying to reduce usage of unit 1151 final usage = 1.02536 477 sec elapsed 0 36 successful, failed patches, couldn't start= 26\n",
      "current avg and SD of usage are 0.99809 0.08521\n",
      "starting to reduce usage for unit 700 with usage 1.02536 . Total units tried = 397\n",
      "try to drop unit 700 from 0 th HD out of 68 possible.  usage down to 1.0253607626387602\n",
      "all done trying to reduce usage of unit 700 final usage = 1.02536 479 sec elapsed 0 52 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99809 0.08521\n",
      "starting to reduce usage for unit 308 with usage 1.02532 . Total units tried = 398\n",
      "try to drop unit 308 from 0 th HD out of 75 possible.  usage down to 1.025318811523785\n",
      "all done trying to reduce usage of unit 308 final usage = 1.01361 479 sec elapsed 1 3 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99809 0.08521\n",
      "starting to reduce usage for unit 676 with usage 1.02527 . Total units tried = 399\n",
      "try to drop unit 676 from 0 th HD out of 81 possible.  usage down to 1.025268470185784\n",
      "all done trying to reduce usage of unit 676 final usage = 1.00923 479 sec elapsed 1 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.9981 0.08521\n",
      "starting to reduce usage for unit 1111 with usage 1.02526 . Total units tried = 400\n",
      "try to drop unit 1111 from 0 th HD out of 81 possible.  usage down to 1.0252600799628635\n",
      "all done trying to reduce usage of unit 1111 final usage = 1.01194 480 sec elapsed 1 3 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99809 0.0852\n",
      "starting to reduce usage for unit 1381 with usage 1.0252 . Total units tried = 401\n",
      "try to drop unit 1381 from 0 th HD out of 71 possible.  usage down to 1.025201348401806\n",
      "all done trying to reduce usage of unit 1381 final usage = 1.01366 480 sec elapsed 1 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99808 0.0852\n",
      "starting to reduce usage for unit 195 with usage 1.02516 . Total units tried = 402\n",
      "try to drop unit 195 from 0 th HD out of 69 possible.  usage down to 1.0251593972868833\n",
      "all done trying to reduce usage of unit 195 final usage = 1.02516 481 sec elapsed 0 35 successful, failed patches, couldn't start= 21\n",
      "current avg and SD of usage are 0.99808 0.0852\n",
      "starting to reduce usage for unit 1022 with usage 1.02491 . Total units tried = 403\n",
      "try to drop unit 1022 from 0 th HD out of 91 possible.  usage down to 1.0249076905969026\n",
      "all done trying to reduce usage of unit 1022 final usage = 1.0138 481 sec elapsed 1 3 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99808 0.0852\n",
      "starting to reduce usage for unit 1191 with usage 1.02477 . Total units tried = 404\n",
      "try to drop unit 1191 from 0 th HD out of 80 possible.  usage down to 1.0247734470288083\n",
      "all done trying to reduce usage of unit 1191 final usage = 1.01155 483 sec elapsed 1 22 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99808 0.0852\n",
      "starting to reduce usage for unit 564 with usage 1.02471 . Total units tried = 405\n",
      "try to drop unit 564 from 0 th HD out of 88 possible.  usage down to 1.024706325244845\n",
      "all done trying to reduce usage of unit 564 final usage = 1.01371 483 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99808 0.0852\n",
      "starting to reduce usage for unit 647 with usage 1.02461 . Total units tried = 406\n",
      "try to drop unit 647 from 0 th HD out of 59 possible.  usage down to 1.0246056425688252\n",
      "all done trying to reduce usage of unit 647 final usage = 1.02461 484 sec elapsed 0 39 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99808 0.0852\n",
      "starting to reduce usage for unit 554 with usage 1.02453 . Total units tried = 407\n",
      "try to drop unit 554 from 0 th HD out of 59 possible.  usage down to 1.0245301305618026\n",
      "all done trying to reduce usage of unit 554 final usage = 1.02453 485 sec elapsed 0 47 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99808 0.0852\n",
      "starting to reduce usage for unit 1471 with usage 1.02451 . Total units tried = 408\n",
      "try to drop unit 1471 from 0 th HD out of 67 possible.  usage down to 1.024513350115783\n",
      "all done trying to reduce usage of unit 1471 final usage = 1.02451 486 sec elapsed 0 38 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 0.99808 0.0852\n",
      "starting to reduce usage for unit 1398 with usage 1.02432 . Total units tried = 409\n",
      "try to drop unit 1398 from 0 th HD out of 60 possible.  usage down to 1.024320374986794\n",
      "all done trying to reduce usage of unit 1398 final usage = 1.01042 486 sec elapsed 1 4 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99807 0.08519\n",
      "starting to reduce usage for unit 830 with usage 1.02429 . Total units tried = 410\n",
      "try to drop unit 830 from 0 th HD out of 89 possible.  usage down to 1.0242868140947883\n",
      "all done trying to reduce usage of unit 830 final usage = 1.01314 487 sec elapsed 1 16 successful, failed patches, couldn't start= 26\n",
      "current avg and SD of usage are 0.99807 0.08518\n",
      "starting to reduce usage for unit 1914 with usage 1.02429 . Total units tried = 411\n",
      "try to drop unit 1914 from 0 th HD out of 61 possible.  usage down to 1.024286814094742\n",
      "all done trying to reduce usage of unit 1914 final usage = 1.01415 487 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99806 0.08518\n",
      "starting to reduce usage for unit 599 with usage 1.0242 . Total units tried = 412\n",
      "try to drop unit 599 from 0 th HD out of 74 possible.  usage down to 1.0242029118650098\n",
      "all done trying to reduce usage of unit 599 final usage = 1.0171 487 sec elapsed 1 6 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99806 0.08517\n",
      "starting to reduce usage for unit 1927 with usage 1.02417 . Total units tried = 413\n",
      "try to drop unit 1927 from 0 th HD out of 87 possible.  usage down to 1.0241693509727086\n",
      "all done trying to reduce usage of unit 1927 final usage = 1.01352 488 sec elapsed 1 5 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99806 0.08518\n",
      "starting to reduce usage for unit 52 with usage 1.02408 . Total units tried = 414\n",
      "try to drop unit 52 from 0 th HD out of 77 possible.  usage down to 1.0240770585198264\n",
      "all done trying to reduce usage of unit 52 final usage = 1.0145 489 sec elapsed 1 3 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99806 0.08517\n",
      "starting to reduce usage for unit 1433 with usage 1.02408 . Total units tried = 415\n",
      "try to drop unit 1433 from 0 th HD out of 41 possible.  usage down to 1.024077058519768\n",
      "all done trying to reduce usage of unit 1433 final usage = 1.01561 489 sec elapsed 1 4 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99806 0.08517\n",
      "starting to reduce usage for unit 745 with usage 1.02407 . Total units tried = 416\n",
      "try to drop unit 745 from 0 th HD out of 56 possible.  usage down to 1.0240686682968256\n",
      "all done trying to reduce usage of unit 745 final usage = 1.00864 489 sec elapsed 1 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99807 0.08517\n",
      "starting to reduce usage for unit 739 with usage 1.02471 . Total units tried = 417\n",
      "try to drop unit 739 from 0 th HD out of 82 possible.  usage down to 1.0247063252448458\n",
      "all done trying to reduce usage of unit 739 final usage = 1.01549 489 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99806 0.08517\n",
      "starting to reduce usage for unit 984 with usage 1.02403 . Total units tried = 418\n",
      "try to drop unit 984 from 0 th HD out of 88 possible.  usage down to 1.0240267171818207\n",
      "all done trying to reduce usage of unit 984 final usage = 1.01491 490 sec elapsed 1 4 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99806 0.08517\n",
      "starting to reduce usage for unit 497 with usage 1.02399 . Total units tried = 419\n",
      "try to drop unit 497 from 0 th HD out of 117 possible.  usage down to 1.0239931562900957\n",
      "try to drop unit 497 from 90 th HD out of 117 possible.  usage down to 1.0239931562900957\n",
      "all done trying to reduce usage of unit 497 final usage = 1.02399 490 sec elapsed 0 69 successful, failed patches, couldn't start= 25\n",
      "current avg and SD of usage are 0.99806 0.08517\n",
      "starting to reduce usage for unit 1413 with usage 1.02395 . Total units tried = 420\n",
      "try to drop unit 1413 from 0 th HD out of 77 possible.  usage down to 1.0239512051747857\n",
      "all done trying to reduce usage of unit 1413 final usage = 1.00768 490 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99806 0.08517\n",
      "starting to reduce usage for unit 1343 with usage 1.02612 . Total units tried = 421\n",
      "try to drop unit 1343 from 0 th HD out of 74 possible.  usage down to 1.026115882708848\n",
      "all done trying to reduce usage of unit 1343 final usage = 1.00985 491 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99806 0.08517\n",
      "starting to reduce usage for unit 805 with usage 1.02394 . Total units tried = 422\n",
      "try to drop unit 805 from 0 th HD out of 85 possible.  usage down to 1.0239428149517877\n",
      "all done trying to reduce usage of unit 805 final usage = 1.02394 492 sec elapsed 0 40 successful, failed patches, couldn't start= 28\n",
      "current avg and SD of usage are 0.99806 0.08517\n",
      "starting to reduce usage for unit 584 with usage 1.0239 . Total units tried = 423\n",
      "try to drop unit 584 from 0 th HD out of 65 possible.  usage down to 1.0239008638366611\n",
      "all done trying to reduce usage of unit 584 final usage = 1.00623 492 sec elapsed 1 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99807 0.08516\n",
      "starting to reduce usage for unit 1384 with usage 1.0238 . Total units tried = 424\n",
      "try to drop unit 1384 from 0 th HD out of 73 possible.  usage down to 1.0238001811607544\n",
      "all done trying to reduce usage of unit 1384 final usage = 1.0238 494 sec elapsed 0 53 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99807 0.08516\n",
      "starting to reduce usage for unit 503 with usage 1.02372 . Total units tried = 425\n",
      "try to drop unit 503 from 0 th HD out of 83 possible.  usage down to 1.023716278930806\n",
      "all done trying to reduce usage of unit 503 final usage = 1.02372 494 sec elapsed 0 63 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99807 0.08516\n",
      "starting to reduce usage for unit 994 with usage 1.02371 . Total units tried = 426\n",
      "try to drop unit 994 from 0 th HD out of 77 possible.  usage down to 1.0237078887077624\n",
      "all done trying to reduce usage of unit 994 final usage = 1.01581 494 sec elapsed 1 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99806 0.08516\n",
      "starting to reduce usage for unit 1361 with usage 1.02369 . Total units tried = 427\n",
      "try to drop unit 1361 from 0 th HD out of 69 possible.  usage down to 1.023691108261758\n",
      "all done trying to reduce usage of unit 1361 final usage = 1.01356 495 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99806 0.08515\n",
      "starting to reduce usage for unit 256 with usage 1.02351 . Total units tried = 428\n",
      "try to drop unit 256 from 0 th HD out of 76 possible.  usage down to 1.0235149135787087\n",
      "all done trying to reduce usage of unit 256 final usage = 1.01405 496 sec elapsed 1 12 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99806 0.08515\n",
      "starting to reduce usage for unit 305 with usage 1.0235 . Total units tried = 429\n",
      "try to drop unit 305 from 0 th HD out of 71 possible.  usage down to 1.0234981331328081\n",
      "all done trying to reduce usage of unit 305 final usage = 1.00538 496 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99805 0.08515\n",
      "starting to reduce usage for unit 658 with usage 1.02348 . Total units tried = 430\n",
      "try to drop unit 658 from 0 th HD out of 87 possible.  usage down to 1.0234813526867204\n",
      "all done trying to reduce usage of unit 658 final usage = 1.01657 496 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99806 0.08515\n",
      "starting to reduce usage for unit 1544 with usage 1.02342 . Total units tried = 431\n",
      "try to drop unit 1544 from 0 th HD out of 46 possible.  usage down to 1.0234226211257562\n",
      "all done trying to reduce usage of unit 1544 final usage = 1.02342 496 sec elapsed 0 24 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 0.99806 0.08515\n",
      "starting to reduce usage for unit 321 with usage 1.02341 . Total units tried = 432\n",
      "try to drop unit 321 from 0 th HD out of 99 possible.  usage down to 1.0234142309029577\n",
      "all done trying to reduce usage of unit 321 final usage = 1.00724 497 sec elapsed 1 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99807 0.08515\n",
      "starting to reduce usage for unit 22 with usage 1.02617 . Total units tried = 433\n",
      "try to drop unit 22 from 0 th HD out of 94 possible.  usage down to 1.0261746142698804\n",
      "all done trying to reduce usage of unit 22 final usage = 1.01371 497 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99806 0.08515\n",
      "starting to reduce usage for unit 1517 with usage 1.02332 . Total units tried = 434\n",
      "try to drop unit 1517 from 0 th HD out of 83 possible.  usage down to 1.0233219384498125\n",
      "all done trying to reduce usage of unit 1517 final usage = 1.02332 498 sec elapsed 0 41 successful, failed patches, couldn't start= 26\n",
      "current avg and SD of usage are 0.99806 0.08515\n",
      "starting to reduce usage for unit 1858 with usage 1.02331 . Total units tried = 435\n",
      "try to drop unit 1858 from 0 th HD out of 87 possible.  usage down to 1.0233051580040025\n",
      "all done trying to reduce usage of unit 1858 final usage = 1.02331 501 sec elapsed 0 55 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.99806 0.08515\n",
      "starting to reduce usage for unit 1878 with usage 1.02323 . Total units tried = 436\n",
      "try to drop unit 1878 from 0 th HD out of 111 possible.  usage down to 1.0232296459969799\n",
      "all done trying to reduce usage of unit 1878 final usage = 1.02323 504 sec elapsed 0 82 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99806 0.08515\n",
      "starting to reduce usage for unit 1123 with usage 1.02323 . Total units tried = 437\n",
      "try to drop unit 1123 from 0 th HD out of 77 possible.  usage down to 1.0232296459967596\n",
      "all done trying to reduce usage of unit 1123 final usage = 1.01121 505 sec elapsed 1 11 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99805 0.08514\n",
      "starting to reduce usage for unit 752 with usage 1.02323 . Total units tried = 438\n",
      "try to drop unit 752 from 0 th HD out of 68 possible.  usage down to 1.0232296459967336\n",
      "all done trying to reduce usage of unit 752 final usage = 1.01208 505 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99806 0.08513\n",
      "starting to reduce usage for unit 1099 with usage 1.02317 . Total units tried = 439\n",
      "try to drop unit 1099 from 0 th HD out of 53 possible.  usage down to 1.023170914435793\n",
      "all done trying to reduce usage of unit 1099 final usage = 1.01208 506 sec elapsed 1 12 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99805 0.08511\n",
      "starting to reduce usage for unit 1215 with usage 1.02317 . Total units tried = 440\n",
      "try to drop unit 1215 from 0 th HD out of 74 possible.  usage down to 1.0231709144357681\n",
      "all done trying to reduce usage of unit 1215 final usage = 1.01208 508 sec elapsed 1 34 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99805 0.08509\n",
      "starting to reduce usage for unit 188 with usage 1.02315 . Total units tried = 441\n",
      "try to drop unit 188 from 0 th HD out of 86 possible.  usage down to 1.0231541339898935\n",
      "all done trying to reduce usage of unit 188 final usage = 1.01224 508 sec elapsed 1 17 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99805 0.08509\n",
      "starting to reduce usage for unit 62 with usage 1.02314 . Total units tried = 442\n",
      "try to drop unit 62 from 0 th HD out of 58 possible.  usage down to 1.0231373535437365\n",
      "all done trying to reduce usage of unit 62 final usage = 1.00866 508 sec elapsed 1 18 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99804 0.08508\n",
      "starting to reduce usage for unit 807 with usage 1.02306 . Total units tried = 443\n",
      "try to drop unit 807 from 0 th HD out of 73 possible.  usage down to 1.0230618415367863\n",
      "all done trying to reduce usage of unit 807 final usage = 1.02306 509 sec elapsed 0 56 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99804 0.08508\n",
      "starting to reduce usage for unit 386 with usage 1.0229 . Total units tried = 444\n",
      "try to drop unit 386 from 0 th HD out of 103 possible.  usage down to 1.022902427299965\n",
      "all done trying to reduce usage of unit 386 final usage = 1.01243 509 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99804 0.08508\n",
      "starting to reduce usage for unit 258 with usage 1.02284 . Total units tried = 445\n",
      "try to drop unit 258 from 0 th HD out of 75 possible.  usage down to 1.0228436957387153\n",
      "all done trying to reduce usage of unit 258 final usage = 1.01408 510 sec elapsed 1 4 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99805 0.08497\n",
      "starting to reduce usage for unit 1243 with usage 1.02284 . Total units tried = 446\n",
      "try to drop unit 1243 from 0 th HD out of 71 possible.  usage down to 1.0228436957387008\n",
      "all done trying to reduce usage of unit 1243 final usage = 1.01163 511 sec elapsed 1 28 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99806 0.08497\n",
      "starting to reduce usage for unit 1833 with usage 1.02275 . Total units tried = 447\n",
      "try to drop unit 1833 from 0 th HD out of 121 possible.  usage down to 1.022751403285989\n",
      "all done trying to reduce usage of unit 1833 final usage = 1.01075 513 sec elapsed 1 19 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99806 0.08497\n",
      "starting to reduce usage for unit 501 with usage 1.02275 . Total units tried = 448\n",
      "try to drop unit 501 from 0 th HD out of 74 possible.  usage down to 1.022751403285762\n",
      "all done trying to reduce usage of unit 501 final usage = 1.00759 513 sec elapsed 1 5 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99806 0.08497\n",
      "starting to reduce usage for unit 379 with usage 1.02273 . Total units tried = 449\n",
      "try to drop unit 379 from 0 th HD out of 65 possible.  usage down to 1.0227262326167872\n",
      "all done trying to reduce usage of unit 379 final usage = 1.00472 513 sec elapsed 1 2 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99806 0.08497\n",
      "starting to reduce usage for unit 352 with usage 1.02789 . Total units tried = 450\n",
      "try to drop unit 352 from 0 th HD out of 75 possible.  usage down to 1.0278862197619496\n",
      "all done trying to reduce usage of unit 352 final usage = 1.01111 513 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99806 0.08496\n",
      "starting to reduce usage for unit 1280 with usage 1.02271 . Total units tried = 451\n",
      "try to drop unit 1280 from 0 th HD out of 80 possible.  usage down to 1.022709452170754\n",
      "all done trying to reduce usage of unit 1280 final usage = 1.00848 514 sec elapsed 1 9 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99805 0.08493\n",
      "starting to reduce usage for unit 1199 with usage 1.02388 . Total units tried = 452\n",
      "try to drop unit 1199 from 0 th HD out of 77 possible.  usage down to 1.0238840833907428\n",
      "all done trying to reduce usage of unit 1199 final usage = 1.02388 516 sec elapsed 0 56 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99805 0.08493\n",
      "starting to reduce usage for unit 1198 with usage 1.02341 . Total units tried = 453\n",
      "try to drop unit 1198 from 0 th HD out of 68 possible.  usage down to 1.0234058406797577\n",
      "all done trying to reduce usage of unit 1198 final usage = 1.02341 518 sec elapsed 0 46 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 0.99805 0.08493\n",
      "starting to reduce usage for unit 1504 with usage 1.02266 . Total units tried = 454\n",
      "try to drop unit 1504 from 0 th HD out of 66 possible.  usage down to 1.02265911083273\n",
      "all done trying to reduce usage of unit 1504 final usage = 1.02266 519 sec elapsed 0 31 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 0.99805 0.08493\n",
      "starting to reduce usage for unit 5 with usage 1.02265 . Total units tried = 455\n",
      "try to drop unit 5 from 0 th HD out of 97 possible.  usage down to 1.022650720609969\n",
      "all done trying to reduce usage of unit 5 final usage = 1.00605 519 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99804 0.08492\n",
      "starting to reduce usage for unit 470 with usage 1.025 . Total units tried = 456\n",
      "try to drop unit 470 from 0 th HD out of 82 possible.  usage down to 1.0249999830497678\n",
      "all done trying to reduce usage of unit 470 final usage = 1.01359 519 sec elapsed 1 5 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99804 0.08492\n",
      "starting to reduce usage for unit 549 with usage 1.02264 . Total units tried = 457\n",
      "try to drop unit 549 from 0 th HD out of 63 possible.  usage down to 1.0226423303868832\n",
      "all done trying to reduce usage of unit 549 final usage = 1.01252 520 sec elapsed 1 8 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99804 0.08492\n",
      "starting to reduce usage for unit 731 with usage 1.02264 . Total units tried = 458\n",
      "try to drop unit 731 from 0 th HD out of 81 possible.  usage down to 1.0226423303867838\n",
      "all done trying to reduce usage of unit 731 final usage = 1.02264 522 sec elapsed 0 57 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99804 0.08492\n",
      "starting to reduce usage for unit 1244 with usage 1.02264 . Total units tried = 459\n",
      "try to drop unit 1244 from 0 th HD out of 79 possible.  usage down to 1.0226423303867185\n",
      "all done trying to reduce usage of unit 1244 final usage = 1.02264 526 sec elapsed 0 48 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 0.99804 0.08492\n",
      "starting to reduce usage for unit 639 with usage 1.02258 . Total units tried = 460\n",
      "try to drop unit 639 from 0 th HD out of 65 possible.  usage down to 1.0225752086028204\n",
      "all done trying to reduce usage of unit 639 final usage = 1.01461 526 sec elapsed 1 6 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99803 0.08492\n",
      "starting to reduce usage for unit 240 with usage 1.02254 . Total units tried = 461\n",
      "try to drop unit 240 from 0 th HD out of 65 possible.  usage down to 1.0225416477106923\n",
      "all done trying to reduce usage of unit 240 final usage = 1.01313 527 sec elapsed 1 9 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99804 0.08492\n",
      "starting to reduce usage for unit 11 with usage 1.02252 . Total units tried = 462\n",
      "try to drop unit 11 from 0 th HD out of 102 possible.  usage down to 1.022524867264904\n",
      "all done trying to reduce usage of unit 11 final usage = 1.00631 527 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99804 0.08491\n",
      "starting to reduce usage for unit 889 with usage 1.02237 . Total units tried = 463\n",
      "try to drop unit 889 from 0 th HD out of 69 possible.  usage down to 1.0223654530276456\n",
      "all done trying to reduce usage of unit 889 final usage = 1.00746 529 sec elapsed 1 24 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99805 0.08491\n",
      "starting to reduce usage for unit 1393 with usage 1.02409 . Total units tried = 464\n",
      "try to drop unit 1393 from 0 th HD out of 76 possible.  usage down to 1.0240854487427948\n",
      "all done trying to reduce usage of unit 1393 final usage = 1.00827 531 sec elapsed 2 28 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99805 0.08491\n",
      "starting to reduce usage for unit 874 with usage 1.02339 . Total units tried = 465\n",
      "try to drop unit 874 from 0 th HD out of 73 possible.  usage down to 1.0233890602336704\n",
      "all done trying to reduce usage of unit 874 final usage = 1.01487 531 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99804 0.08491\n",
      "starting to reduce usage for unit 123 with usage 1.02235 . Total units tried = 466\n",
      "try to drop unit 123 from 0 th HD out of 66 possible.  usage down to 1.0223486725816604\n",
      "all done trying to reduce usage of unit 123 final usage = 1.01093 532 sec elapsed 1 39 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99804 0.08491\n",
      "starting to reduce usage for unit 1345 with usage 1.02232 . Total units tried = 467\n",
      "try to drop unit 1345 from 0 th HD out of 73 possible.  usage down to 1.022315111689706\n",
      "all done trying to reduce usage of unit 1345 final usage = 1.02232 533 sec elapsed 0 37 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 0.99804 0.08491\n",
      "starting to reduce usage for unit 823 with usage 1.02228 . Total units tried = 468\n",
      "try to drop unit 823 from 0 th HD out of 60 possible.  usage down to 1.0222815507977214\n",
      "all done trying to reduce usage of unit 823 final usage = 1.02228 533 sec elapsed 0 43 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99804 0.08491\n",
      "starting to reduce usage for unit 1251 with usage 1.02226 . Total units tried = 469\n",
      "try to drop unit 1251 from 0 th HD out of 57 possible.  usage down to 1.0222563801286788\n",
      "all done trying to reduce usage of unit 1251 final usage = 1.00881 534 sec elapsed 1 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99803 0.0849\n",
      "starting to reduce usage for unit 1180 with usage 1.02341 . Total units tried = 470\n",
      "try to drop unit 1180 from 0 th HD out of 63 possible.  usage down to 1.0234058406797883\n",
      "all done trying to reduce usage of unit 1180 final usage = 1.00209 535 sec elapsed 1 18 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99802 0.08489\n",
      "starting to reduce usage for unit 866 with usage 1.03111 . Total units tried = 471\n",
      "try to drop unit 866 from 0 th HD out of 73 possible.  usage down to 1.031108065393961\n",
      "all done trying to reduce usage of unit 866 final usage = 1.03111 537 sec elapsed 0 32 successful, failed patches, couldn't start= 27\n",
      "current avg and SD of usage are 0.99802 0.08489\n",
      "starting to reduce usage for unit 872 with usage 1.03106 . Total units tried = 472\n",
      "try to drop unit 872 from 0 th HD out of 55 possible.  usage down to 1.031057724056004\n",
      "all done trying to reduce usage of unit 872 final usage = 1.00983 538 sec elapsed 2 17 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 0.99802 0.08488\n",
      "starting to reduce usage for unit 1353 with usage 1.031 . Total units tried = 473\n",
      "try to drop unit 1353 from 0 th HD out of 69 possible.  usage down to 1.0309989924949987\n",
      "all done trying to reduce usage of unit 1353 final usage = 1.01784 539 sec elapsed 1 2 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99802 0.08488\n",
      "starting to reduce usage for unit 881 with usage 1.02834 . Total units tried = 474\n",
      "try to drop unit 881 from 0 th HD out of 77 possible.  usage down to 1.028339291803831\n",
      "all done trying to reduce usage of unit 881 final usage = 1.01236 539 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99801 0.08488\n",
      "starting to reduce usage for unit 932 with usage 1.02736 . Total units tried = 475\n",
      "try to drop unit 932 from 0 th HD out of 73 possible.  usage down to 1.0273576357128091\n",
      "all done trying to reduce usage of unit 932 final usage = 1.01598 540 sec elapsed 1 3 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99801 0.08488\n",
      "starting to reduce usage for unit 1357 with usage 1.02631 . Total units tried = 476\n",
      "try to drop unit 1357 from 0 th HD out of 69 possible.  usage down to 1.0263088578378912\n",
      "all done trying to reduce usage of unit 1357 final usage = 1.01606 540 sec elapsed 1 4 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.998 0.08488\n",
      "starting to reduce usage for unit 857 with usage 1.0257 . Total units tried = 477\n",
      "try to drop unit 857 from 0 th HD out of 67 possible.  usage down to 1.0257047617818051\n",
      "all done trying to reduce usage of unit 857 final usage = 1.01437 542 sec elapsed 1 16 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99799 0.08487\n",
      "starting to reduce usage for unit 90 with usage 1.0222 . Total units tried = 478\n",
      "try to drop unit 90 from 0 th HD out of 80 possible.  usage down to 1.0221976485676891\n",
      "all done trying to reduce usage of unit 90 final usage = 1.0059 542 sec elapsed 1 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99798 0.08487\n",
      "starting to reduce usage for unit 1134 with usage 1.02217 . Total units tried = 479\n",
      "try to drop unit 1134 from 0 th HD out of 66 possible.  usage down to 1.022172477898696\n",
      "all done trying to reduce usage of unit 1134 final usage = 1.01104 543 sec elapsed 1 2 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99798 0.08487\n",
      "starting to reduce usage for unit 1196 with usage 1.02214 . Total units tried = 480\n",
      "try to drop unit 1196 from 0 th HD out of 80 possible.  usage down to 1.0221389170067037\n",
      "all done trying to reduce usage of unit 1196 final usage = 1.01216 544 sec elapsed 1 25 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99798 0.08487\n",
      "starting to reduce usage for unit 1788 with usage 1.02214 . Total units tried = 481\n",
      "try to drop unit 1788 from 0 th HD out of 83 possible.  usage down to 1.022138917006603\n",
      "all done trying to reduce usage of unit 1788 final usage = 1.00828 545 sec elapsed 1 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99797 0.08487\n",
      "starting to reduce usage for unit 291 with usage 1.02212 . Total units tried = 482\n",
      "try to drop unit 291 from 0 th HD out of 90 possible.  usage down to 1.022122136560831\n",
      "all done trying to reduce usage of unit 291 final usage = 1.02212 545 sec elapsed 0 65 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99797 0.08487\n",
      "starting to reduce usage for unit 1065 with usage 1.02212 . Total units tried = 483\n",
      "try to drop unit 1065 from 0 th HD out of 59 possible.  usage down to 1.0221221365607263\n",
      "all done trying to reduce usage of unit 1065 final usage = 1.0153 546 sec elapsed 1 2 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99797 0.08487\n",
      "starting to reduce usage for unit 1515 with usage 1.02206 . Total units tried = 484\n",
      "try to drop unit 1515 from 0 th HD out of 62 possible.  usage down to 1.0220550147767324\n",
      "all done trying to reduce usage of unit 1515 final usage = 1.02206 547 sec elapsed 0 48 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99797 0.08487\n",
      "starting to reduce usage for unit 1391 with usage 1.02198 . Total units tried = 485\n",
      "try to drop unit 1391 from 0 th HD out of 61 possible.  usage down to 1.0219795027696692\n",
      "all done trying to reduce usage of unit 1391 final usage = 1.01666 549 sec elapsed 1 31 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99797 0.08486\n",
      "starting to reduce usage for unit 671 with usage 1.0219 . Total units tried = 486\n",
      "try to drop unit 671 from 0 th HD out of 64 possible.  usage down to 1.021903990762724\n",
      "all done trying to reduce usage of unit 671 final usage = 1.0219 550 sec elapsed 0 41 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99797 0.08486\n",
      "starting to reduce usage for unit 1269 with usage 1.0219 . Total units tried = 487\n",
      "try to drop unit 1269 from 0 th HD out of 83 possible.  usage down to 1.0218956005397293\n",
      "all done trying to reduce usage of unit 1269 final usage = 1.01246 550 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99796 0.08486\n",
      "starting to reduce usage for unit 1909 with usage 1.02186 . Total units tried = 488\n",
      "try to drop unit 1909 from 0 th HD out of 71 possible.  usage down to 1.0218620396477136\n",
      "all done trying to reduce usage of unit 1909 final usage = 1.02186 553 sec elapsed 0 38 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 0.99796 0.08486\n",
      "starting to reduce usage for unit 191 with usage 1.02185 . Total units tried = 489\n",
      "try to drop unit 191 from 0 th HD out of 88 possible.  usage down to 1.021845259201757\n",
      "all done trying to reduce usage of unit 191 final usage = 1.00831 554 sec elapsed 1 48 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99797 0.08484\n",
      "starting to reduce usage for unit 1720 with usage 1.02182 . Total units tried = 490\n",
      "try to drop unit 1720 from 0 th HD out of 89 possible.  usage down to 1.021820088532687\n",
      "all done trying to reduce usage of unit 1720 final usage = 1.02182 557 sec elapsed 0 46 successful, failed patches, couldn't start= 26\n",
      "current avg and SD of usage are 0.99797 0.08484\n",
      "starting to reduce usage for unit 527 with usage 1.0218 . Total units tried = 491\n",
      "try to drop unit 527 from 0 th HD out of 99 possible.  usage down to 1.0218033080868687\n",
      "all done trying to reduce usage of unit 527 final usage = 1.00369 557 sec elapsed 1 5 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99796 0.08483\n",
      "starting to reduce usage for unit 669 with usage 1.02174 . Total units tried = 492\n",
      "try to drop unit 669 from 0 th HD out of 86 possible.  usage down to 1.0217361863026824\n",
      "all done trying to reduce usage of unit 669 final usage = 1.01066 558 sec elapsed 1 6 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99795 0.08483\n",
      "starting to reduce usage for unit 921 with usage 1.0217 . Total units tried = 493\n",
      "try to drop unit 921 from 0 th HD out of 71 possible.  usage down to 1.0217026254107016\n",
      "all done trying to reduce usage of unit 921 final usage = 1.0217 559 sec elapsed 0 37 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 0.99795 0.08483\n",
      "starting to reduce usage for unit 50 with usage 1.02169 . Total units tried = 494\n",
      "try to drop unit 50 from 0 th HD out of 87 possible.  usage down to 1.021694235187848\n",
      "all done trying to reduce usage of unit 50 final usage = 1.01445 559 sec elapsed 1 6 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99795 0.08483\n",
      "starting to reduce usage for unit 2 with usage 1.02159 . Total units tried = 495\n",
      "try to drop unit 2 from 0 th HD out of 75 possible.  usage down to 1.0215935525116646\n",
      "all done trying to reduce usage of unit 2 final usage = 1.00584 559 sec elapsed 1 6 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99795 0.08463\n",
      "starting to reduce usage for unit 883 with usage 1.02155 . Total units tried = 496\n",
      "try to drop unit 883 from 0 th HD out of 70 possible.  usage down to 1.021551601396683\n",
      "all done trying to reduce usage of unit 883 final usage = 1.01271 561 sec elapsed 1 21 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 0.99795 0.08463\n",
      "starting to reduce usage for unit 21 with usage 1.02151 . Total units tried = 497\n",
      "try to drop unit 21 from 0 th HD out of 66 possible.  usage down to 1.0215096502816574\n",
      "all done trying to reduce usage of unit 21 final usage = 1.00893 561 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99794 0.08463\n",
      "starting to reduce usage for unit 803 with usage 1.02147 . Total units tried = 498\n",
      "try to drop unit 803 from 0 th HD out of 53 possible.  usage down to 1.0214676991667846\n",
      "all done trying to reduce usage of unit 803 final usage = 1.02147 562 sec elapsed 0 28 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.99794 0.08463\n",
      "starting to reduce usage for unit 734 with usage 1.02147 . Total units tried = 499\n",
      "try to drop unit 734 from 0 th HD out of 81 possible.  usage down to 1.0214676991667366\n",
      "all done trying to reduce usage of unit 734 final usage = 1.01382 562 sec elapsed 1 6 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99794 0.08461\n",
      "starting to reduce usage for unit 340 with usage 1.02138 . Total units tried = 500\n",
      "try to drop unit 340 from 0 th HD out of 85 possible.  usage down to 1.021383796936788\n",
      "all done trying to reduce usage of unit 340 final usage = 1.00556 562 sec elapsed 1 5 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99794 0.08461\n",
      "starting to reduce usage for unit 598 with usage 1.02552 . Total units tried = 501\n",
      "try to drop unit 598 from 0 th HD out of 58 possible.  usage down to 1.0255201768758357\n",
      "all done trying to reduce usage of unit 598 final usage = 1.01238 563 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99793 0.08461\n",
      "starting to reduce usage for unit 1922 with usage 1.02133 . Total units tried = 502\n",
      "try to drop unit 1922 from 0 th HD out of 62 possible.  usage down to 1.02133345559871\n",
      "all done trying to reduce usage of unit 1922 final usage = 1.01257 563 sec elapsed 1 3 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99793 0.08461\n",
      "starting to reduce usage for unit 1549 with usage 1.02127 . Total units tried = 503\n",
      "try to drop unit 1549 from 0 th HD out of 73 possible.  usage down to 1.0212747240377122\n",
      "all done trying to reduce usage of unit 1549 final usage = 1.02127 564 sec elapsed 0 45 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99793 0.08461\n",
      "starting to reduce usage for unit 108 with usage 1.02127 . Total units tried = 504\n",
      "try to drop unit 108 from 0 th HD out of 94 possible.  usage down to 1.021266333814742\n",
      "all done trying to reduce usage of unit 108 final usage = 1.01311 564 sec elapsed 1 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99793 0.08461\n",
      "starting to reduce usage for unit 1082 with usage 1.02122 . Total units tried = 505\n",
      "try to drop unit 1082 from 0 th HD out of 68 possible.  usage down to 1.0212243826997254\n",
      "all done trying to reduce usage of unit 1082 final usage = 1.00788 565 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99793 0.08461\n",
      "starting to reduce usage for unit 54 with usage 1.02115 . Total units tried = 506\n",
      "try to drop unit 54 from 0 th HD out of 56 possible.  usage down to 1.0211488706926384\n",
      "all done trying to reduce usage of unit 54 final usage = 1.00348 565 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99792 0.08461\n",
      "starting to reduce usage for unit 691 with usage 1.02174 . Total units tried = 507\n",
      "try to drop unit 691 from 0 th HD out of 91 possible.  usage down to 1.0217361863027392\n",
      "all done trying to reduce usage of unit 691 final usage = 1.00623 565 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99793 0.08442\n",
      "starting to reduce usage for unit 1028 with usage 1.02109 . Total units tried = 508\n",
      "try to drop unit 1028 from 0 th HD out of 66 possible.  usage down to 1.0210901391317153\n",
      "all done trying to reduce usage of unit 1028 final usage = 1.00774 566 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99793 0.08442\n",
      "starting to reduce usage for unit 222 with usage 1.02108 . Total units tried = 509\n",
      "try to drop unit 222 from 0 th HD out of 84 possible.  usage down to 1.021081748908695\n",
      "all done trying to reduce usage of unit 222 final usage = 1.00789 566 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99792 0.08441\n",
      "starting to reduce usage for unit 806 with usage 1.02107 . Total units tried = 510\n",
      "try to drop unit 806 from 0 th HD out of 75 possible.  usage down to 1.021073358685729\n",
      "all done trying to reduce usage of unit 806 final usage = 0.99884 566 sec elapsed 1 5 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99791 0.08441\n",
      "starting to reduce usage for unit 781 with usage 1.02785 . Total units tried = 511\n",
      "try to drop unit 781 from 0 th HD out of 73 possible.  usage down to 1.0278526588699002\n",
      "all done trying to reduce usage of unit 781 final usage = 1.01471 567 sec elapsed 1 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9979 0.08441\n",
      "starting to reduce usage for unit 525 with usage 1.02627 . Total units tried = 512\n",
      "try to drop unit 525 from 0 th HD out of 94 possible.  usage down to 1.0262669067230092\n",
      "all done trying to reduce usage of unit 525 final usage = 1.01742 567 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9979 0.08441\n",
      "starting to reduce usage for unit 811 with usage 1.02145 . Total units tried = 513\n",
      "try to drop unit 811 from 0 th HD out of 80 possible.  usage down to 1.0214509187207144\n",
      "all done trying to reduce usage of unit 811 final usage = 1.01098 568 sec elapsed 1 58 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.9979 0.08441\n",
      "starting to reduce usage for unit 1980 with usage 1.02104 . Total units tried = 514\n",
      "try to drop unit 1980 from 0 th HD out of 52 possible.  usage down to 1.021039797793923\n",
      "all done trying to reduce usage of unit 1980 final usage = 1.01277 571 sec elapsed 1 24 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99789 0.08441\n",
      "starting to reduce usage for unit 1382 with usage 1.02103 . Total units tried = 515\n",
      "try to drop unit 1382 from 0 th HD out of 63 possible.  usage down to 1.0210314075706937\n",
      "all done trying to reduce usage of unit 1382 final usage = 1.02103 573 sec elapsed 0 51 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99789 0.08441\n",
      "starting to reduce usage for unit 910 with usage 1.02103 . Total units tried = 516\n",
      "try to drop unit 910 from 0 th HD out of 69 possible.  usage down to 1.021031407570622\n",
      "all done trying to reduce usage of unit 910 final usage = 1.02103 574 sec elapsed 0 40 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 0.99789 0.08441\n",
      "starting to reduce usage for unit 888 with usage 1.02101 . Total units tried = 517\n",
      "try to drop unit 888 from 0 th HD out of 52 possible.  usage down to 1.0210146271246796\n",
      "all done trying to reduce usage of unit 888 final usage = 1.00968 575 sec elapsed 1 5 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99789 0.08441\n",
      "starting to reduce usage for unit 1712 with usage 1.02091 . Total units tried = 518\n",
      "try to drop unit 1712 from 0 th HD out of 58 possible.  usage down to 1.020905554225943\n",
      "all done trying to reduce usage of unit 1712 final usage = 1.02091 579 sec elapsed 0 33 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99789 0.08441\n",
      "starting to reduce usage for unit 638 with usage 1.02091 . Total units tried = 519\n",
      "try to drop unit 638 from 0 th HD out of 50 possible.  usage down to 1.0209055542257373\n",
      "all done trying to reduce usage of unit 638 final usage = 1.00668 579 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9979 0.08441\n",
      "starting to reduce usage for unit 675 with usage 1.02312 . Total units tried = 520\n",
      "try to drop unit 675 from 0 th HD out of 70 possible.  usage down to 1.023120573097815\n",
      "all done trying to reduce usage of unit 675 final usage = 1.01361 580 sec elapsed 1 9 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.9979 0.08441\n",
      "starting to reduce usage for unit 674 with usage 1.02138 . Total units tried = 521\n",
      "try to drop unit 674 from 0 th HD out of 93 possible.  usage down to 1.0213837969367634\n",
      "all done trying to reduce usage of unit 674 final usage = 1.00855 580 sec elapsed 1 2 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99789 0.0844\n",
      "starting to reduce usage for unit 849 with usage 1.02086 . Total units tried = 522\n",
      "try to drop unit 849 from 0 th HD out of 80 possible.  usage down to 1.0208636031107736\n",
      "all done trying to reduce usage of unit 849 final usage = 0.99055 581 sec elapsed 1 18 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99787 0.0844\n",
      "starting to reduce usage for unit 821 with usage 1.03679 . Total units tried = 523\n",
      "try to drop unit 821 from 0 th HD out of 74 possible.  usage down to 1.0367882463651554\n",
      "all done trying to reduce usage of unit 821 final usage = 1.01298 581 sec elapsed 2 5 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99787 0.0844\n",
      "starting to reduce usage for unit 839 with usage 1.02336 . Total units tried = 524\n",
      "try to drop unit 839 from 0 th HD out of 87 possible.  usage down to 1.0233554993417346\n",
      "all done trying to reduce usage of unit 839 final usage = 1.01265 581 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99787 0.0844\n",
      "starting to reduce usage for unit 815 with usage 1.021 . Total units tried = 525\n",
      "try to drop unit 815 from 0 th HD out of 86 possible.  usage down to 1.0209978466787182\n",
      "all done trying to reduce usage of unit 815 final usage = 1.01005 582 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99787 0.0844\n",
      "starting to reduce usage for unit 982 with usage 1.02086 . Total units tried = 526\n",
      "try to drop unit 982 from 0 th HD out of 92 possible.  usage down to 1.0208552128877137\n",
      "all done trying to reduce usage of unit 982 final usage = 1.0041 583 sec elapsed 1 7 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99788 0.08439\n",
      "starting to reduce usage for unit 64 with usage 1.02081 . Total units tried = 527\n",
      "try to drop unit 64 from 0 th HD out of 110 possible.  usage down to 1.020813261772872\n",
      "all done trying to reduce usage of unit 64 final usage = 1.00464 583 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99787 0.08439\n",
      "starting to reduce usage for unit 401 with usage 1.02436 . Total units tried = 528\n",
      "try to drop unit 401 from 0 th HD out of 75 possible.  usage down to 1.024362326101751\n",
      "all done trying to reduce usage of unit 401 final usage = 1.01325 583 sec elapsed 1 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99786 0.08439\n",
      "starting to reduce usage for unit 1076 with usage 1.02179 . Total units tried = 529\n",
      "try to drop unit 1076 from 0 th HD out of 76 possible.  usage down to 1.0217949178637238\n",
      "all done trying to reduce usage of unit 1076 final usage = 1.00562 584 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99787 0.08439\n",
      "starting to reduce usage for unit 809 with usage 1.02078 . Total units tried = 530\n",
      "try to drop unit 809 from 0 th HD out of 74 possible.  usage down to 1.0207797008806878\n",
      "all done trying to reduce usage of unit 809 final usage = 1.00826 584 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99787 0.08437\n",
      "starting to reduce usage for unit 1924 with usage 1.02073 . Total units tried = 531\n",
      "try to drop unit 1924 from 0 th HD out of 83 possible.  usage down to 1.0207293595426763\n",
      "all done trying to reduce usage of unit 1924 final usage = 1.00851 586 sec elapsed 1 9 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99787 0.08437\n",
      "starting to reduce usage for unit 1897 with usage 1.02219 . Total units tried = 532\n",
      "try to drop unit 1897 from 0 th HD out of 43 possible.  usage down to 1.0221892583449768\n",
      "all done trying to reduce usage of unit 1897 final usage = 1.01288 587 sec elapsed 1 8 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99787 0.08437\n",
      "starting to reduce usage for unit 1791 with usage 1.02139 . Total units tried = 533\n",
      "try to drop unit 1791 from 0 th HD out of 76 possible.  usage down to 1.0213921871599612\n",
      "all done trying to reduce usage of unit 1791 final usage = 1.01205 589 sec elapsed 1 13 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99786 0.08433\n",
      "starting to reduce usage for unit 1953 with usage 1.0213 . Total units tried = 534\n",
      "try to drop unit 1953 from 0 th HD out of 81 possible.  usage down to 1.0212998947066376\n",
      "all done trying to reduce usage of unit 1953 final usage = 1.00908 589 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99786 0.08433\n",
      "starting to reduce usage for unit 831 with usage 1.02073 . Total units tried = 535\n",
      "try to drop unit 831 from 0 th HD out of 49 possible.  usage down to 1.0207293595426663\n",
      "all done trying to reduce usage of unit 831 final usage = 1.00663 589 sec elapsed 1 2 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99785 0.08433\n",
      "starting to reduce usage for unit 672 with usage 1.02073 . Total units tried = 536\n",
      "try to drop unit 672 from 0 th HD out of 68 possible.  usage down to 1.0207293595426574\n",
      "all done trying to reduce usage of unit 672 final usage = 1.00798 590 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99784 0.08433\n",
      "starting to reduce usage for unit 668 with usage 1.02063 . Total units tried = 537\n",
      "try to drop unit 668 from 0 th HD out of 87 possible.  usage down to 1.0206286768667328\n",
      "all done trying to reduce usage of unit 668 final usage = 1.00714 590 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99784 0.08433\n",
      "starting to reduce usage for unit 36 with usage 1.02062 . Total units tried = 538\n",
      "try to drop unit 36 from 0 th HD out of 106 possible.  usage down to 1.020620286643822\n",
      "all done trying to reduce usage of unit 36 final usage = 1.0097 590 sec elapsed 1 6 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99784 0.08433\n",
      "starting to reduce usage for unit 1290 with usage 1.02059 . Total units tried = 539\n",
      "try to drop unit 1290 from 0 th HD out of 77 possible.  usage down to 1.0205867257516212\n",
      "all done trying to reduce usage of unit 1290 final usage = 1.00186 592 sec elapsed 1 34 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99784 0.08433\n",
      "starting to reduce usage for unit 1301 with usage 1.02785 . Total units tried = 540\n",
      "try to drop unit 1301 from 0 th HD out of 72 possible.  usage down to 1.0278526588698425\n",
      "all done trying to reduce usage of unit 1301 final usage = 1.01371 594 sec elapsed 1 7 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99784 0.08433\n",
      "starting to reduce usage for unit 1259 with usage 1.02534 . Total units tried = 541\n",
      "try to drop unit 1259 from 0 th HD out of 84 possible.  usage down to 1.0253439821928256\n",
      "all done trying to reduce usage of unit 1259 final usage = 1.02534 599 sec elapsed 0 62 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.99784 0.08433\n",
      "starting to reduce usage for unit 1216 with usage 1.02521 . Total units tried = 542\n",
      "try to drop unit 1216 from 0 th HD out of 79 possible.  usage down to 1.025209738624775\n",
      "all done trying to reduce usage of unit 1216 final usage = 1.01406 602 sec elapsed 1 21 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99783 0.08433\n",
      "starting to reduce usage for unit 1276 with usage 1.02377 . Total units tried = 543\n",
      "try to drop unit 1276 from 0 th HD out of 85 possible.  usage down to 1.023766620268805\n",
      "all done trying to reduce usage of unit 1276 final usage = 1.00504 603 sec elapsed 1 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99783 0.08433\n",
      "starting to reduce usage for unit 1160 with usage 1.02857 . Total units tried = 544\n",
      "try to drop unit 1160 from 0 th HD out of 77 possible.  usage down to 1.0285658278249206\n",
      "all done trying to reduce usage of unit 1160 final usage = 1.02005 608 sec elapsed 1 42 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 0.99783 0.08433\n",
      "starting to reduce usage for unit 1365 with usage 1.02794 . Total units tried = 545\n",
      "try to drop unit 1365 from 0 th HD out of 81 possible.  usage down to 1.0279365610999036\n",
      "all done trying to reduce usage of unit 1365 final usage = 1.01379 609 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99783 0.08433\n",
      "starting to reduce usage for unit 1131 with usage 1.02534 . Total units tried = 546\n",
      "try to drop unit 1131 from 0 th HD out of 65 possible.  usage down to 1.0253355919698313\n",
      "all done trying to reduce usage of unit 1131 final usage = 1.00661 610 sec elapsed 1 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99783 0.08433\n",
      "starting to reduce usage for unit 1333 with usage 1.02528 . Total units tried = 547\n",
      "try to drop unit 1333 from 0 th HD out of 56 possible.  usage down to 1.0252768604087532\n",
      "all done trying to reduce usage of unit 1333 final usage = 1.00655 610 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99783 0.08433\n",
      "starting to reduce usage for unit 870 with usage 1.02411 . Total units tried = 548\n",
      "try to drop unit 870 from 0 th HD out of 53 possible.  usage down to 1.0241106194117542\n",
      "all done trying to reduce usage of unit 870 final usage = 1.00996 611 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99783 0.08433\n",
      "starting to reduce usage for unit 1260 with usage 1.02274 . Total units tried = 549\n",
      "try to drop unit 1260 from 0 th HD out of 76 possible.  usage down to 1.0227430130626902\n",
      "all done trying to reduce usage of unit 1260 final usage = 1.0138 614 sec elapsed 1 10 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99782 0.08433\n",
      "starting to reduce usage for unit 1132 with usage 1.02252 . Total units tried = 550\n",
      "try to drop unit 1132 from 0 th HD out of 64 possible.  usage down to 1.022524867264734\n",
      "all done trying to reduce usage of unit 1132 final usage = 1.0038 616 sec elapsed 1 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99782 0.08433\n",
      "starting to reduce usage for unit 871 with usage 1.02387 . Total units tried = 551\n",
      "try to drop unit 871 from 0 th HD out of 53 possible.  usage down to 1.0238673029447563\n",
      "all done trying to reduce usage of unit 871 final usage = 1.00514 616 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99782 0.08433\n",
      "starting to reduce usage for unit 1395 with usage 1.0225 . Total units tried = 552\n",
      "try to drop unit 1395 from 0 th HD out of 81 possible.  usage down to 1.0224996965957251\n",
      "all done trying to reduce usage of unit 1395 final usage = 1.00377 617 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99782 0.08433\n",
      "starting to reduce usage for unit 1387 with usage 1.02199 . Total units tried = 553\n",
      "try to drop unit 1387 from 0 th HD out of 74 possible.  usage down to 1.021987892992728\n",
      "all done trying to reduce usage of unit 1387 final usage = 1.00809 618 sec elapsed 1 12 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99781 0.08433\n",
      "starting to reduce usage for unit 1310 with usage 1.02138 . Total units tried = 554\n",
      "try to drop unit 1310 from 0 th HD out of 74 possible.  usage down to 1.0213837969367119\n",
      "all done trying to reduce usage of unit 1310 final usage = 1.00266 619 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99782 0.08433\n",
      "starting to reduce usage for unit 863 with usage 1.02603 . Total units tried = 555\n",
      "try to drop unit 863 from 0 th HD out of 73 possible.  usage down to 1.0260319804788716\n",
      "all done trying to reduce usage of unit 863 final usage = 1.00731 619 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99781 0.08433\n",
      "starting to reduce usage for unit 1322 with usage 1.02116 . Total units tried = 556\n",
      "try to drop unit 1322 from 0 th HD out of 70 possible.  usage down to 1.0211572609156745\n",
      "all done trying to reduce usage of unit 1322 final usage = 1.00701 621 sec elapsed 1 2 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99781 0.08433\n",
      "starting to reduce usage for unit 1227 with usage 1.02286 . Total units tried = 557\n",
      "try to drop unit 1227 from 0 th HD out of 84 possible.  usage down to 1.0228604761847815\n",
      "all done trying to reduce usage of unit 1227 final usage = 1.01313 621 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99781 0.0843\n",
      "starting to reduce usage for unit 1142 with usage 1.02086 . Total units tried = 558\n",
      "try to drop unit 1142 from 0 th HD out of 77 possible.  usage down to 1.0208636031106784\n",
      "all done trying to reduce usage of unit 1142 final usage = 1.00889 624 sec elapsed 1 23 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.9978 0.0843\n",
      "starting to reduce usage for unit 1324 with usage 1.02061 . Total units tried = 559\n",
      "try to drop unit 1324 from 0 th HD out of 70 possible.  usage down to 1.0206118964206867\n",
      "all done trying to reduce usage of unit 1324 final usage = 1.00751 624 sec elapsed 1 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99779 0.0843\n",
      "starting to reduce usage for unit 1228 with usage 1.02262 . Total units tried = 560\n",
      "try to drop unit 1228 from 0 th HD out of 54 possible.  usage down to 1.022617159717738\n",
      "all done trying to reduce usage of unit 1228 final usage = 1.01318 625 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99779 0.0843\n",
      "starting to reduce usage for unit 1352 with usage 1.02223 . Total units tried = 561\n",
      "try to drop unit 1352 from 0 th HD out of 81 possible.  usage down to 1.0222312094597177\n",
      "all done trying to reduce usage of unit 1352 final usage = 1.00913 625 sec elapsed 1 1 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99779 0.0843\n",
      "starting to reduce usage for unit 604 with usage 1.0212 . Total units tried = 562\n",
      "try to drop unit 604 from 0 th HD out of 76 possible.  usage down to 1.0211992120306677\n",
      "all done trying to reduce usage of unit 604 final usage = 1.00809 626 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99779 0.0843\n",
      "starting to reduce usage for unit 1358 with usage 1.02133 . Total units tried = 563\n",
      "try to drop unit 1358 from 0 th HD out of 58 possible.  usage down to 1.0213250653757115\n",
      "all done trying to reduce usage of unit 1358 final usage = 1.00854 627 sec elapsed 1 8 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99779 0.0843\n",
      "starting to reduce usage for unit 894 with usage 1.02201 . Total units tried = 564\n",
      "try to drop unit 894 from 0 th HD out of 54 possible.  usage down to 1.0220130636617406\n",
      "all done trying to reduce usage of unit 894 final usage = 1.00923 627 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99779 0.0843\n",
      "starting to reduce usage for unit 1349 with usage 1.02095 . Total units tried = 565\n",
      "try to drop unit 1349 from 0 th HD out of 60 possible.  usage down to 1.0209475053407076\n",
      "all done trying to reduce usage of unit 1349 final usage = 1.01141 628 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99779 0.0843\n",
      "starting to reduce usage for unit 1168 with usage 1.02065 . Total units tried = 566\n",
      "try to drop unit 1168 from 0 th HD out of 56 possible.  usage down to 1.0206454573126462\n",
      "all done trying to reduce usage of unit 1168 final usage = 1.00754 628 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99778 0.0843\n",
      "starting to reduce usage for unit 1261 with usage 1.02063 . Total units tried = 567\n",
      "try to drop unit 1261 from 0 th HD out of 86 possible.  usage down to 1.0206286768666732\n",
      "all done trying to reduce usage of unit 1261 final usage = 1.00956 629 sec elapsed 1 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99779 0.0843\n",
      "starting to reduce usage for unit 1204 with usage 1.02086 . Total units tried = 568\n",
      "try to drop unit 1204 from 0 th HD out of 84 possible.  usage down to 1.0208552128876687\n",
      "all done trying to reduce usage of unit 1204 final usage = 1.00976 629 sec elapsed 1 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99779 0.0843\n",
      "starting to reduce usage for unit 175 with usage 1.02054 . Total units tried = 569\n",
      "try to drop unit 175 from 0 th HD out of 91 possible.  usage down to 1.0205447746367307\n",
      "all done trying to reduce usage of unit 175 final usage = 1.00918 629 sec elapsed 1 5 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99779 0.08429\n",
      "starting to reduce usage for unit 761 with usage 1.02051 . Total units tried = 570\n",
      "try to drop unit 761 from 0 th HD out of 87 possible.  usage down to 1.0205112137446637\n",
      "all done trying to reduce usage of unit 761 final usage = 1.00929 630 sec elapsed 1 3 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99778 0.08429\n",
      "starting to reduce usage for unit 897 with usage 1.02049 . Total units tried = 571\n",
      "try to drop unit 897 from 0 th HD out of 69 possible.  usage down to 1.0204860430757454\n",
      "all done trying to reduce usage of unit 897 final usage = 1.00733 630 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99778 0.08429\n",
      "starting to reduce usage for unit 1411 with usage 1.0231 . Total units tried = 572\n",
      "try to drop unit 1411 from 0 th HD out of 53 possible.  usage down to 1.0230954024286916\n",
      "all done trying to reduce usage of unit 1411 final usage = 1.01296 631 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99778 0.08429\n",
      "starting to reduce usage for unit 868 with usage 1.02095 . Total units tried = 573\n",
      "try to drop unit 868 from 0 th HD out of 78 possible.  usage down to 1.0209475053406514\n",
      "all done trying to reduce usage of unit 868 final usage = 1.0068 633 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99778 0.08429\n",
      "starting to reduce usage for unit 935 with usage 1.02321 . Total units tried = 574\n",
      "try to drop unit 935 from 0 th HD out of 74 possible.  usage down to 1.0232128655506545\n",
      "all done trying to reduce usage of unit 935 final usage = 1.00907 636 sec elapsed 1 9 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99778 0.08429\n",
      "starting to reduce usage for unit 1430 with usage 1.02077 . Total units tried = 575\n",
      "try to drop unit 1430 from 0 th HD out of 68 possible.  usage down to 1.0207713106576422\n",
      "all done trying to reduce usage of unit 1430 final usage = 1.00755 637 sec elapsed 1 3 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99777 0.08429\n",
      "starting to reduce usage for unit 1419 with usage 1.02049 . Total units tried = 576\n",
      "try to drop unit 1419 from 0 th HD out of 78 possible.  usage down to 1.0204860430756673\n",
      "all done trying to reduce usage of unit 1419 final usage = 1.00757 637 sec elapsed 1 6 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99778 0.08426\n",
      "starting to reduce usage for unit 808 with usage 1.02046 . Total units tried = 577\n",
      "try to drop unit 808 from 0 th HD out of 77 possible.  usage down to 1.0204608724067088\n",
      "all done trying to reduce usage of unit 808 final usage = 1.00945 638 sec elapsed 1 11 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99777 0.08426\n",
      "starting to reduce usage for unit 822 with usage 1.02056 . Total units tried = 578\n",
      "try to drop unit 822 from 0 th HD out of 60 possible.  usage down to 1.0205615550826734\n",
      "all done trying to reduce usage of unit 822 final usage = 1.00741 638 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99778 0.08426\n",
      "starting to reduce usage for unit 846 with usage 1.0227 . Total units tried = 579\n",
      "try to drop unit 846 from 0 th HD out of 80 possible.  usage down to 1.0227010619477834\n",
      "all done trying to reduce usage of unit 846 final usage = 1.00994 638 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99778 0.08425\n",
      "starting to reduce usage for unit 552 with usage 1.02258 . Total units tried = 580\n",
      "try to drop unit 552 from 0 th HD out of 83 possible.  usage down to 1.0225752086027797\n",
      "all done trying to reduce usage of unit 552 final usage = 1.00314 639 sec elapsed 1 12 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99778 0.08426\n",
      "starting to reduce usage for unit 850 with usage 1.02598 . Total units tried = 581\n",
      "try to drop unit 850 from 0 th HD out of 88 possible.  usage down to 1.0259816391408947\n",
      "all done trying to reduce usage of unit 850 final usage = 1.01594 639 sec elapsed 1 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99777 0.08425\n",
      "starting to reduce usage for unit 851 with usage 1.02506 . Total units tried = 582\n",
      "try to drop unit 851 from 0 th HD out of 89 possible.  usage down to 1.0250587146108754\n",
      "all done trying to reduce usage of unit 851 final usage = 1.01919 640 sec elapsed 1 3 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99778 0.08425\n",
      "starting to reduce usage for unit 843 with usage 1.02498 . Total units tried = 583\n",
      "try to drop unit 843 from 0 th HD out of 50 possible.  usage down to 1.024983202603855\n",
      "all done trying to reduce usage of unit 843 final usage = 1.01319 640 sec elapsed 1 8 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99777 0.08425\n",
      "starting to reduce usage for unit 307 with usage 1.02411 . Total units tried = 584\n",
      "try to drop unit 307 from 0 th HD out of 99 possible.  usage down to 1.0241106194118674\n",
      "all done trying to reduce usage of unit 307 final usage = 1.00467 640 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99777 0.08425\n",
      "starting to reduce usage for unit 794 with usage 1.02543 . Total units tried = 585\n",
      "try to drop unit 794 from 0 th HD out of 56 possible.  usage down to 1.0254278844228895\n",
      "all done trying to reduce usage of unit 794 final usage = 0.97093 641 sec elapsed 1 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99771 0.0843\n",
      "starting to reduce usage for unit 791 with usage 1.06375 . Total units tried = 586\n",
      "try to drop unit 791 from 0 th HD out of 87 possible.  usage down to 1.063754423088015\n",
      "all done trying to reduce usage of unit 791 final usage = 1.01174 642 sec elapsed 4 46 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99769 0.08427\n",
      "starting to reduce usage for unit 778 with usage 1.06104 . Total units tried = 587\n",
      "try to drop unit 778 from 0 th HD out of 62 possible.  usage down to 1.0610359908359077\n",
      "all done trying to reduce usage of unit 778 final usage = 1.0159 642 sec elapsed 4 8 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99769 0.08425\n",
      "starting to reduce usage for unit 771 with usage 1.05628 . Total units tried = 588\n",
      "try to drop unit 771 from 0 th HD out of 62 possible.  usage down to 1.0562787343947866\n",
      "all done trying to reduce usage of unit 771 final usage = 1.01987 643 sec elapsed 3 7 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99766 0.08423\n",
      "starting to reduce usage for unit 817 with usage 1.05343 . Total units tried = 589\n",
      "try to drop unit 817 from 0 th HD out of 47 possible.  usage down to 1.0534260585747037\n",
      "all done trying to reduce usage of unit 817 final usage = 1.01164 643 sec elapsed 3 4 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99765 0.08423\n",
      "starting to reduce usage for unit 818 with usage 1.03323 . Total units tried = 590\n",
      "try to drop unit 818 from 0 th HD out of 60 possible.  usage down to 1.0332307918131078\n",
      "all done trying to reduce usage of unit 818 final usage = 1.01849 644 sec elapsed 1 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99766 0.08423\n",
      "starting to reduce usage for unit 759 with usage 1.02465 . Total units tried = 591\n",
      "try to drop unit 759 from 0 th HD out of 73 possible.  usage down to 1.0246475936838109\n",
      "all done trying to reduce usage of unit 759 final usage = 1.01491 644 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99767 0.08423\n",
      "starting to reduce usage for unit 825 with usage 1.02456 . Total units tried = 592\n",
      "try to drop unit 825 from 0 th HD out of 57 possible.  usage down to 1.0245636914538332\n",
      "all done trying to reduce usage of unit 825 final usage = 1.00936 645 sec elapsed 1 5 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99766 0.08422\n",
      "starting to reduce usage for unit 760 with usage 1.02336 . Total units tried = 593\n",
      "try to drop unit 760 from 0 th HD out of 90 possible.  usage down to 1.0233638895647488\n",
      "all done trying to reduce usage of unit 760 final usage = 1.00862 645 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99765 0.08422\n",
      "starting to reduce usage for unit 842 with usage 1.0232 . Total units tried = 594\n",
      "try to drop unit 842 from 0 th HD out of 59 possible.  usage down to 1.0231960851047774\n",
      "all done trying to reduce usage of unit 842 final usage = 1.00479 645 sec elapsed 1 3 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99764 0.08422\n",
      "starting to reduce usage for unit 756 with usage 1.02247 . Total units tried = 595\n",
      "try to drop unit 756 from 0 th HD out of 48 possible.  usage down to 1.0224745259267274\n",
      "all done trying to reduce usage of unit 756 final usage = 1.01195 646 sec elapsed 1 1 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99764 0.08422\n",
      "starting to reduce usage for unit 753 with usage 1.02237 . Total units tried = 596\n",
      "try to drop unit 753 from 0 th HD out of 52 possible.  usage down to 1.0223654530277686\n",
      "all done trying to reduce usage of unit 753 final usage = 1.00928 647 sec elapsed 1 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99764 0.08422\n",
      "starting to reduce usage for unit 770 with usage 1.02254 . Total units tried = 597\n",
      "try to drop unit 770 from 0 th HD out of 66 possible.  usage down to 1.0225416477107019\n",
      "all done trying to reduce usage of unit 770 final usage = 1.01132 647 sec elapsed 1 2 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99764 0.08422\n",
      "starting to reduce usage for unit 804 with usage 1.02175 . Total units tried = 598\n",
      "try to drop unit 804 from 0 th HD out of 90 possible.  usage down to 1.0217529667487486\n",
      "all done trying to reduce usage of unit 804 final usage = 1.01105 648 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99764 0.08422\n",
      "starting to reduce usage for unit 796 with usage 1.02073 . Total units tried = 599\n",
      "try to drop unit 796 from 0 th HD out of 89 possible.  usage down to 1.0207293595426898\n",
      "all done trying to reduce usage of unit 796 final usage = 1.01142 648 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99764 0.08422\n",
      "starting to reduce usage for unit 1958 with usage 1.02044 . Total units tried = 600\n",
      "try to drop unit 1958 from 0 th HD out of 68 possible.  usage down to 1.0204440919606605\n",
      "all done trying to reduce usage of unit 1958 final usage = 1.01006 650 sec elapsed 1 9 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99763 0.08422\n",
      "starting to reduce usage for unit 572 with usage 1.02043 . Total units tried = 601\n",
      "try to drop unit 572 from 0 th HD out of 63 possible.  usage down to 1.0204273115146638\n",
      "all done trying to reduce usage of unit 572 final usage = 1.00231 650 sec elapsed 1 8 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99763 0.08422\n",
      "starting to reduce usage for unit 906 with usage 1.02448 . Total units tried = 602\n",
      "try to drop unit 906 from 0 th HD out of 71 possible.  usage down to 1.024479789223852\n",
      "all done trying to reduce usage of unit 906 final usage = 1.01335 651 sec elapsed 1 15 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99763 0.08422\n",
      "starting to reduce usage for unit 918 with usage 1.02157 . Total units tried = 603\n",
      "try to drop unit 918 from 0 th HD out of 74 possible.  usage down to 1.0215683818427461\n",
      "all done trying to reduce usage of unit 918 final usage = 1.0011 651 sec elapsed 1 9 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99763 0.08422\n",
      "starting to reduce usage for unit 891 with usage 1.02305 . Total units tried = 604\n",
      "try to drop unit 891 from 0 th HD out of 49 possible.  usage down to 1.0230534513138185\n",
      "all done trying to reduce usage of unit 891 final usage = 1.01419 652 sec elapsed 1 10 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99762 0.08422\n",
      "starting to reduce usage for unit 925 with usage 1.02143 . Total units tried = 605\n",
      "try to drop unit 925 from 0 th HD out of 57 possible.  usage down to 1.0214341382747247\n",
      "all done trying to reduce usage of unit 925 final usage = 1.00096 653 sec elapsed 1 2 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99762 0.08422\n",
      "starting to reduce usage for unit 938 with usage 1.02943 . Total units tried = 606\n",
      "try to drop unit 938 from 0 th HD out of 77 possible.  usage down to 1.0294300207939973\n",
      "all done trying to reduce usage of unit 938 final usage = 1.01061 653 sec elapsed 1 22 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99762 0.08422\n",
      "starting to reduce usage for unit 939 with usage 1.02803 . Total units tried = 607\n",
      "try to drop unit 939 from 0 th HD out of 74 possible.  usage down to 1.0280288535529192\n",
      "all done trying to reduce usage of unit 939 final usage = 1.0121 654 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99761 0.08422\n",
      "starting to reduce usage for unit 864 with usage 1.02127 . Total units tried = 608\n",
      "try to drop unit 864 from 0 th HD out of 79 possible.  usage down to 1.0212747240377107\n",
      "all done trying to reduce usage of unit 864 final usage = 0.99938 654 sec elapsed 1 1 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.9976 0.08422\n",
      "starting to reduce usage for unit 893 with usage 1.03071 . Total units tried = 609\n",
      "try to drop unit 893 from 0 th HD out of 73 possible.  usage down to 1.0307053346900212\n",
      "all done trying to reduce usage of unit 893 final usage = 1.01072 655 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9976 0.08422\n",
      "starting to reduce usage for unit 892 with usage 1.02502 . Total units tried = 610\n",
      "try to drop unit 892 from 0 th HD out of 71 possible.  usage down to 1.0250167634958507\n",
      "all done trying to reduce usage of unit 892 final usage = 1.01288 656 sec elapsed 1 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99759 0.08421\n",
      "starting to reduce usage for unit 280 with usage 1.02188 . Total units tried = 611\n",
      "try to drop unit 280 from 0 th HD out of 71 possible.  usage down to 1.0218788200938231\n",
      "all done trying to reduce usage of unit 280 final usage = 1.00316 656 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99758 0.08421\n",
      "starting to reduce usage for unit 875 with usage 1.02663 . Total units tried = 612\n",
      "try to drop unit 875 from 0 th HD out of 73 possible.  usage down to 1.026627686311888\n",
      "all done trying to reduce usage of unit 875 final usage = 1.0107 656 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99759 0.08421\n",
      "starting to reduce usage for unit 614 with usage 1.02464 . Total units tried = 613\n",
      "try to drop unit 614 from 0 th HD out of 73 possible.  usage down to 1.0246392034608438\n",
      "all done trying to reduce usage of unit 614 final usage = 1.00652 656 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99759 0.08422\n",
      "starting to reduce usage for unit 940 with usage 1.02646 . Total units tried = 614\n",
      "try to drop unit 940 from 0 th HD out of 57 possible.  usage down to 1.0264598818518604\n",
      "all done trying to reduce usage of unit 940 final usage = 1.00835 656 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99759 0.08421\n",
      "starting to reduce usage for unit 613 with usage 1.02282 . Total units tried = 615\n",
      "try to drop unit 613 from 0 th HD out of 60 possible.  usage down to 1.022818525069821\n",
      "all done trying to reduce usage of unit 613 final usage = 1.00689 657 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99759 0.08421\n",
      "starting to reduce usage for unit 855 with usage 1.02138 . Total units tried = 616\n",
      "try to drop unit 855 from 0 th HD out of 58 possible.  usage down to 1.0213754067137626\n",
      "all done trying to reduce usage of unit 855 final usage = 1.00229 658 sec elapsed 1 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99758 0.08422\n",
      "starting to reduce usage for unit 1410 with usage 1.02755 . Total units tried = 617\n",
      "try to drop unit 1410 from 0 th HD out of 68 possible.  usage down to 1.0275506108418553\n",
      "all done trying to reduce usage of unit 1410 final usage = 1.01439 658 sec elapsed 1 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99758 0.08421\n",
      "starting to reduce usage for unit 933 with usage 1.02258 . Total units tried = 618\n",
      "try to drop unit 933 from 0 th HD out of 76 possible.  usage down to 1.0225752086027773\n",
      "all done trying to reduce usage of unit 933 final usage = 1.01244 659 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99758 0.08421\n",
      "starting to reduce usage for unit 905 with usage 1.02188 . Total units tried = 619\n",
      "try to drop unit 905 from 0 th HD out of 70 possible.  usage down to 1.0218788200936961\n",
      "all done trying to reduce usage of unit 905 final usage = 1.00084 660 sec elapsed 1 24 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 0.99758 0.08422\n",
      "starting to reduce usage for unit 886 with usage 1.03022 . Total units tried = 620\n",
      "try to drop unit 886 from 0 th HD out of 54 possible.  usage down to 1.030218701755954\n",
      "all done trying to reduce usage of unit 886 final usage = 1.01884 661 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99758 0.08421\n",
      "starting to reduce usage for unit 879 with usage 1.03013 . Total units tried = 621\n",
      "try to drop unit 879 from 0 th HD out of 77 possible.  usage down to 1.0301264093029328\n",
      "all done trying to reduce usage of unit 879 final usage = 1.01669 661 sec elapsed 1 4 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 0.99757 0.08421\n",
      "starting to reduce usage for unit 865 with usage 1.02924 . Total units tried = 622\n",
      "try to drop unit 865 from 0 th HD out of 70 possible.  usage down to 1.0292370456649318\n",
      "all done trying to reduce usage of unit 865 final usage = 1.0191 662 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99757 0.08421\n",
      "starting to reduce usage for unit 869 with usage 1.02322 . Total units tried = 623\n",
      "try to drop unit 869 from 0 th HD out of 61 possible.  usage down to 1.0232212557737232\n",
      "all done trying to reduce usage of unit 869 final usage = 1.01471 663 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99757 0.08421\n",
      "starting to reduce usage for unit 927 with usage 1.02085 . Total units tried = 624\n",
      "try to drop unit 927 from 0 th HD out of 74 possible.  usage down to 1.0208468226645588\n",
      "all done trying to reduce usage of unit 927 final usage = 1.00759 663 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99757 0.08421\n",
      "starting to reduce usage for unit 979 with usage 1.0204 . Total units tried = 625\n",
      "try to drop unit 979 from 0 th HD out of 56 possible.  usage down to 1.0204021408457167\n",
      "all done trying to reduce usage of unit 979 final usage = 1.00705 664 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99757 0.08421\n",
      "starting to reduce usage for unit 1190 with usage 1.0204 . Total units tried = 626\n",
      "try to drop unit 1190 from 0 th HD out of 57 possible.  usage down to 1.0204021408456596\n",
      "all done trying to reduce usage of unit 1190 final usage = 1.00709 665 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99757 0.08421\n",
      "starting to reduce usage for unit 890 with usage 1.02154 . Total units tried = 627\n",
      "try to drop unit 890 from 0 th HD out of 59 possible.  usage down to 1.0215432111736706\n",
      "all done trying to reduce usage of unit 890 final usage = 1.01179 665 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99757 0.08421\n",
      "starting to reduce usage for unit 711 with usage 1.02037 . Total units tried = 628\n",
      "try to drop unit 711 from 0 th HD out of 49 possible.  usage down to 1.0203685799537612\n",
      "all done trying to reduce usage of unit 711 final usage = 1.01051 665 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99757 0.08421\n",
      "starting to reduce usage for unit 1001 with usage 1.02037 . Total units tried = 629\n",
      "try to drop unit 1001 from 0 th HD out of 51 possible.  usage down to 1.020368579953696\n",
      "all done trying to reduce usage of unit 1001 final usage = 1.0054 665 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99757 0.08421\n",
      "starting to reduce usage for unit 1080 with usage 1.02043 . Total units tried = 630\n",
      "try to drop unit 1080 from 0 th HD out of 89 possible.  usage down to 1.0204273115146902\n",
      "all done trying to reduce usage of unit 1080 final usage = 1.00786 666 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99757 0.08421\n",
      "starting to reduce usage for unit 1090 with usage 1.02232 . Total units tried = 631\n",
      "try to drop unit 1090 from 0 th HD out of 91 possible.  usage down to 1.0223151116897808\n",
      "all done trying to reduce usage of unit 1090 final usage = 1.01354 667 sec elapsed 1 8 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99758 0.08421\n",
      "starting to reduce usage for unit 717 with usage 1.02034 . Total units tried = 632\n",
      "try to drop unit 717 from 0 th HD out of 73 possible.  usage down to 1.0203350190617633\n",
      "all done trying to reduce usage of unit 717 final usage = 1.00829 667 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99758 0.08421\n",
      "starting to reduce usage for unit 172 with usage 1.02032 . Total units tried = 633\n",
      "try to drop unit 172 from 0 th HD out of 92 possible.  usage down to 1.0203182386157144\n",
      "all done trying to reduce usage of unit 172 final usage = 1.00861 667 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99759 0.08421\n",
      "starting to reduce usage for unit 644 with usage 1.02028 . Total units tried = 634\n",
      "try to drop unit 644 from 0 th HD out of 49 possible.  usage down to 1.020284677723737\n",
      "all done trying to reduce usage of unit 644 final usage = 1.00921 667 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99759 0.08421\n",
      "starting to reduce usage for unit 1519 with usage 1.02023 . Total units tried = 635\n",
      "try to drop unit 1519 from 0 th HD out of 85 possible.  usage down to 1.0202343363857056\n",
      "all done trying to reduce usage of unit 1519 final usage = 1.02023 668 sec elapsed 0 54 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 0.99759 0.08421\n",
      "starting to reduce usage for unit 299 with usage 1.02022 . Total units tried = 636\n",
      "try to drop unit 299 from 0 th HD out of 82 possible.  usage down to 1.020217555939702\n",
      "all done trying to reduce usage of unit 299 final usage = 1.0033 668 sec elapsed 1 15 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99757 0.08421\n",
      "starting to reduce usage for unit 1171 with usage 1.02019 . Total units tried = 637\n",
      "try to drop unit 1171 from 0 th HD out of 69 possible.  usage down to 1.0201923852706236\n",
      "all done trying to reduce usage of unit 1171 final usage = 1.00427 669 sec elapsed 1 16 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99757 0.08421\n",
      "starting to reduce usage for unit 1255 with usage 1.02456 . Total units tried = 638\n",
      "try to drop unit 1255 from 0 th HD out of 76 possible.  usage down to 1.0245553012307536\n",
      "all done trying to reduce usage of unit 1255 final usage = 1.00863 670 sec elapsed 1 2 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99757 0.08421\n",
      "starting to reduce usage for unit 1325 with usage 1.02591 . Total units tried = 639\n",
      "try to drop unit 1325 from 0 th HD out of 55 possible.  usage down to 1.0259145173568356\n",
      "all done trying to reduce usage of unit 1325 final usage = 1.00983 670 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99756 0.08421\n",
      "starting to reduce usage for unit 1334 with usage 1.02465 . Total units tried = 640\n",
      "try to drop unit 1334 from 0 th HD out of 85 possible.  usage down to 1.0246475936838262\n",
      "all done trying to reduce usage of unit 1334 final usage = 1.01357 671 sec elapsed 1 3 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99755 0.08421\n",
      "starting to reduce usage for unit 1364 with usage 1.02314 . Total units tried = 641\n",
      "try to drop unit 1364 from 0 th HD out of 82 possible.  usage down to 1.023137353543766\n",
      "all done trying to reduce usage of unit 1364 final usage = 1.00988 671 sec elapsed 1 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99755 0.08421\n",
      "starting to reduce usage for unit 920 with usage 1.02284 . Total units tried = 642\n",
      "try to drop unit 920 from 0 th HD out of 62 possible.  usage down to 1.022835305515788\n",
      "all done trying to reduce usage of unit 920 final usage = 1.00968 672 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99755 0.08421\n",
      "starting to reduce usage for unit 941 with usage 1.02301 . Total units tried = 643\n",
      "try to drop unit 941 from 0 th HD out of 69 possible.  usage down to 1.0230115001987645\n",
      "all done trying to reduce usage of unit 941 final usage = 1.01121 672 sec elapsed 1 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99754 0.08421\n",
      "starting to reduce usage for unit 1145 with usage 1.02263 . Total units tried = 644\n",
      "try to drop unit 1145 from 0 th HD out of 67 possible.  usage down to 1.0226339401637157\n",
      "all done trying to reduce usage of unit 1145 final usage = 1.01411 672 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99754 0.0842\n",
      "starting to reduce usage for unit 877 with usage 1.02232 . Total units tried = 645\n",
      "try to drop unit 877 from 0 th HD out of 81 possible.  usage down to 1.0223235019127574\n",
      "all done trying to reduce usage of unit 877 final usage = 1.00956 673 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99755 0.08421\n",
      "starting to reduce usage for unit 1256 with usage 1.02264 . Total units tried = 646\n",
      "try to drop unit 1256 from 0 th HD out of 62 possible.  usage down to 1.0226423303867236\n",
      "all done trying to reduce usage of unit 1256 final usage = 1.00789 673 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99755 0.0842\n",
      "starting to reduce usage for unit 1262 with usage 1.02466 . Total units tried = 647\n",
      "try to drop unit 1262 from 0 th HD out of 64 possible.  usage down to 1.0246559839068066\n",
      "all done trying to reduce usage of unit 1262 final usage = 1.00574 673 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99754 0.08419\n",
      "starting to reduce usage for unit 1144 with usage 1.02786 . Total units tried = 648\n",
      "try to drop unit 1144 from 0 th HD out of 69 possible.  usage down to 1.0278610490928561\n",
      "all done trying to reduce usage of unit 1144 final usage = 1.00895 674 sec elapsed 1 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99754 0.08418\n",
      "starting to reduce usage for unit 1292 with usage 1.02413 . Total units tried = 649\n",
      "try to drop unit 1292 from 0 th HD out of 67 possible.  usage down to 1.0241273998577727\n",
      "all done trying to reduce usage of unit 1292 final usage = 1.01021 674 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99754 0.08418\n",
      "starting to reduce usage for unit 895 with usage 1.02182 . Total units tried = 650\n",
      "try to drop unit 895 from 0 th HD out of 73 possible.  usage down to 1.0218200885327382\n",
      "all done trying to reduce usage of unit 895 final usage = 1.00866 675 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99754 0.08418\n",
      "starting to reduce usage for unit 858 with usage 1.02152 . Total units tried = 651\n",
      "try to drop unit 858 from 0 th HD out of 76 possible.  usage down to 1.0215180405046531\n",
      "all done trying to reduce usage of unit 858 final usage = 1.01226 675 sec elapsed 1 1 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99754 0.08419\n",
      "starting to reduce usage for unit 1295 with usage 1.02075 . Total units tried = 652\n",
      "try to drop unit 1295 from 0 th HD out of 65 possible.  usage down to 1.020754530211706\n",
      "all done trying to reduce usage of unit 1295 final usage = 1.00685 676 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99754 0.08418\n",
      "starting to reduce usage for unit 1396 with usage 1.02174 . Total units tried = 653\n",
      "try to drop unit 1396 from 0 th HD out of 66 possible.  usage down to 1.021736186302722\n",
      "all done trying to reduce usage of unit 1396 final usage = 1.0123 676 sec elapsed 1 1 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99753 0.08418\n",
      "starting to reduce usage for unit 916 with usage 1.02064 . Total units tried = 654\n",
      "try to drop unit 916 from 0 th HD out of 76 possible.  usage down to 1.0206370670897122\n",
      "all done trying to reduce usage of unit 916 final usage = 1.00155 678 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99753 0.08418\n",
      "starting to reduce usage for unit 876 with usage 1.02634 . Total units tried = 655\n",
      "try to drop unit 876 from 0 th HD out of 78 possible.  usage down to 1.0263424187298322\n",
      "all done trying to reduce usage of unit 876 final usage = 1.00725 678 sec elapsed 1 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99753 0.08418\n",
      "starting to reduce usage for unit 835 with usage 1.02018 . Total units tried = 656\n",
      "try to drop unit 835 from 0 th HD out of 88 possible.  usage down to 1.0201756048247108\n",
      "all done trying to reduce usage of unit 835 final usage = 1.00267 678 sec elapsed 1 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99753 0.08417\n",
      "starting to reduce usage for unit 832 with usage 1.02732 . Total units tried = 657\n",
      "try to drop unit 832 from 0 th HD out of 77 possible.  usage down to 1.027315684597881\n",
      "all done trying to reduce usage of unit 832 final usage = 1.02732 680 sec elapsed 0 36 successful, failed patches, couldn't start= 26\n",
      "current avg and SD of usage are 0.99753 0.08417\n",
      "starting to reduce usage for unit 838 with usage 1.02672 . Total units tried = 658\n",
      "try to drop unit 838 from 0 th HD out of 70 possible.  usage down to 1.0267199787649368\n",
      "all done trying to reduce usage of unit 838 final usage = 1.00921 680 sec elapsed 1 6 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99752 0.08418\n",
      "starting to reduce usage for unit 787 with usage 1.02617 . Total units tried = 659\n",
      "try to drop unit 787 from 0 th HD out of 73 possible.  usage down to 1.0261662240469085\n",
      "all done trying to reduce usage of unit 787 final usage = 1.00866 681 sec elapsed 1 10 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99753 0.08417\n",
      "starting to reduce usage for unit 763 with usage 1.02451 . Total units tried = 660\n",
      "try to drop unit 763 from 0 th HD out of 57 possible.  usage down to 1.0245133501158008\n",
      "all done trying to reduce usage of unit 763 final usage = 1.01239 682 sec elapsed 1 24 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99752 0.08417\n",
      "starting to reduce usage for unit 814 with usage 1.02398 . Total units tried = 661\n",
      "try to drop unit 814 from 0 th HD out of 82 possible.  usage down to 1.0239763758437688\n",
      "all done trying to reduce usage of unit 814 final usage = 1.0146 682 sec elapsed 1 3 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99752 0.08417\n",
      "starting to reduce usage for unit 757 with usage 1.0221 . Total units tried = 662\n",
      "try to drop unit 757 from 0 th HD out of 78 possible.  usage down to 1.022096965891721\n",
      "all done trying to reduce usage of unit 757 final usage = 1.01033 682 sec elapsed 1 6 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99752 0.08417\n",
      "starting to reduce usage for unit 919 with usage 1.02015 . Total units tried = 663\n",
      "try to drop unit 919 from 0 th HD out of 83 possible.  usage down to 1.020150434155695\n",
      "all done trying to reduce usage of unit 919 final usage = 1.00908 683 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99752 0.08417\n",
      "starting to reduce usage for unit 1394 with usage 1.02099 . Total units tried = 664\n",
      "try to drop unit 1394 from 0 th HD out of 77 possible.  usage down to 1.0209894564556734\n",
      "all done trying to reduce usage of unit 1394 final usage = 1.00831 683 sec elapsed 1 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99752 0.08417\n",
      "starting to reduce usage for unit 1356 with usage 1.02136 . Total units tried = 665\n",
      "try to drop unit 1356 from 0 th HD out of 68 possible.  usage down to 1.0213586262676186\n",
      "all done trying to reduce usage of unit 1356 final usage = 1.00791 684 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99752 0.08417\n",
      "starting to reduce usage for unit 1194 with usage 1.02269 . Total units tried = 666\n",
      "try to drop unit 1194 from 0 th HD out of 62 possible.  usage down to 1.0226926717247558\n",
      "all done trying to reduce usage of unit 1194 final usage = 1.0052 684 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99752 0.08417\n",
      "starting to reduce usage for unit 924 with usage 1.02493 . Total units tried = 667\n",
      "try to drop unit 924 from 0 th HD out of 68 possible.  usage down to 1.0249328612657775\n",
      "all done trying to reduce usage of unit 924 final usage = 1.00621 685 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99753 0.08418\n",
      "starting to reduce usage for unit 1424 with usage 1.02763 . Total units tried = 668\n",
      "try to drop unit 1424 from 0 th HD out of 79 possible.  usage down to 1.0276261228488879\n",
      "all done trying to reduce usage of unit 1424 final usage = 1.01729 685 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99752 0.08418\n",
      "starting to reduce usage for unit 1425 with usage 1.0257 . Total units tried = 669\n",
      "try to drop unit 1425 from 0 th HD out of 77 possible.  usage down to 1.0257047617818311\n",
      "all done trying to reduce usage of unit 1425 final usage = 1.00698 686 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99752 0.08417\n",
      "starting to reduce usage for unit 1320 with usage 1.02246 . Total units tried = 670\n",
      "try to drop unit 1320 from 0 th HD out of 77 possible.  usage down to 1.0224577454806902\n",
      "all done trying to reduce usage of unit 1320 final usage = 1.01064 686 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.99752 0.08417\n",
      "starting to reduce usage for unit 860 with usage 1.02226 . Total units tried = 671\n",
      "try to drop unit 860 from 0 th HD out of 68 possible.  usage down to 1.0222647703516956\n",
      "all done trying to reduce usage of unit 860 final usage = 1.00812 687 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99752 0.08417\n",
      "starting to reduce usage for unit 907 with usage 1.0217 . Total units tried = 672\n",
      "try to drop unit 907 from 0 th HD out of 81 possible.  usage down to 1.021702625410727\n",
      "all done trying to reduce usage of unit 907 final usage = 1.01157 689 sec elapsed 1 14 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99752 0.08417\n",
      "starting to reduce usage for unit 1323 with usage 1.02092 . Total units tried = 673\n",
      "try to drop unit 1323 from 0 th HD out of 76 possible.  usage down to 1.0209223346715972\n",
      "all done trying to reduce usage of unit 1323 final usage = 1.00678 690 sec elapsed 1 1 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99752 0.08417\n",
      "starting to reduce usage for unit 899 with usage 1.02081 . Total units tried = 674\n",
      "try to drop unit 899 from 0 th HD out of 76 possible.  usage down to 1.0208132617726908\n",
      "all done trying to reduce usage of unit 899 final usage = 1.01108 691 sec elapsed 1 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99751 0.08416\n",
      "starting to reduce usage for unit 1355 with usage 1.0203 . Total units tried = 675\n",
      "try to drop unit 1355 from 0 th HD out of 80 possible.  usage down to 1.0203014581696936\n",
      "all done trying to reduce usage of unit 1355 final usage = 1.00685 692 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08416\n",
      "starting to reduce usage for unit 1363 with usage 1.02331 . Total units tried = 676\n",
      "try to drop unit 1363 from 0 th HD out of 51 possible.  usage down to 1.0233051580036927\n",
      "all done trying to reduce usage of unit 1363 final usage = 1.00986 693 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08416\n",
      "starting to reduce usage for unit 434 with usage 1.02013 . Total units tried = 677\n",
      "try to drop unit 434 from 0 th HD out of 44 possible.  usage down to 1.0201252634866902\n",
      "all done trying to reduce usage of unit 434 final usage = 1.02013 693 sec elapsed 0 17 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 0.99751 0.08416\n",
      "starting to reduce usage for unit 466 with usage 1.02007 . Total units tried = 678\n",
      "try to drop unit 466 from 0 th HD out of 68 possible.  usage down to 1.0200665319255615\n",
      "all done trying to reduce usage of unit 466 final usage = 1.00457 693 sec elapsed 1 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.9975 0.08416\n",
      "starting to reduce usage for unit 543 with usage 1.02006 . Total units tried = 679\n",
      "try to drop unit 543 from 0 th HD out of 99 possible.  usage down to 1.020058141702782\n",
      "all done trying to reduce usage of unit 543 final usage = 1.00842 693 sec elapsed 1 7 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.9975 0.08416\n",
      "starting to reduce usage for unit 1416 with usage 1.02005 . Total units tried = 680\n",
      "try to drop unit 1416 from 0 th HD out of 78 possible.  usage down to 1.0200497514796663\n",
      "all done trying to reduce usage of unit 1416 final usage = 1.0089 694 sec elapsed 1 1 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 0.9975 0.08416\n",
      "starting to reduce usage for unit 724 with usage 1.02004 . Total units tried = 681\n",
      "try to drop unit 724 from 0 th HD out of 90 possible.  usage down to 1.0200413612567052\n",
      "all done trying to reduce usage of unit 724 final usage = 1.00705 694 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9975 0.08416\n",
      "starting to reduce usage for unit 707 with usage 1.02065 . Total units tried = 682\n",
      "try to drop unit 707 from 0 th HD out of 65 possible.  usage down to 1.0206538475356615\n",
      "all done trying to reduce usage of unit 707 final usage = 1.00902 694 sec elapsed 1 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9975 0.08416\n",
      "starting to reduce usage for unit 750 with usage 1.02071 . Total units tried = 683\n",
      "try to drop unit 750 from 0 th HD out of 78 possible.  usage down to 1.0207125790967644\n",
      "all done trying to reduce usage of unit 750 final usage = 1.00788 695 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08401\n",
      "starting to reduce usage for unit 1804 with usage 1.02003 . Total units tried = 684\n",
      "try to drop unit 1804 from 0 th HD out of 86 possible.  usage down to 1.0200329710335254\n",
      "all done trying to reduce usage of unit 1804 final usage = 1.00667 695 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 1083 with usage 1.01994 . Total units tried = 685\n",
      "all done trying to reduce usage of unit 1083 final usage = 1.01994 695 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 762 with usage 1.01994 . Total units tried = 686\n",
      "all done trying to reduce usage of unit 762 final usage = 1.01994 695 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 296 with usage 1.01994 . Total units tried = 687\n",
      "all done trying to reduce usage of unit 296 final usage = 1.01994 695 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 1561 with usage 1.01986 . Total units tried = 688\n",
      "all done trying to reduce usage of unit 1561 final usage = 1.01986 696 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 1176 with usage 1.01985 . Total units tried = 689\n",
      "all done trying to reduce usage of unit 1176 final usage = 1.01985 696 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 494 with usage 1.01982 . Total units tried = 690\n",
      "all done trying to reduce usage of unit 494 final usage = 1.01982 696 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 110 with usage 1.01982 . Total units tried = 691\n",
      "all done trying to reduce usage of unit 110 final usage = 1.01982 696 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 1884 with usage 1.01978 . Total units tried = 692\n",
      "all done trying to reduce usage of unit 1884 final usage = 1.01978 696 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 164 with usage 1.01976 . Total units tried = 693\n",
      "all done trying to reduce usage of unit 164 final usage = 1.01976 697 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 1661 with usage 1.01975 . Total units tried = 694\n",
      "all done trying to reduce usage of unit 1661 final usage = 1.01975 697 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 109 with usage 1.01975 . Total units tried = 695\n",
      "all done trying to reduce usage of unit 109 final usage = 1.01975 697 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 482 with usage 1.01973 . Total units tried = 696\n",
      "all done trying to reduce usage of unit 482 final usage = 1.01973 697 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 114 with usage 1.01971 . Total units tried = 697\n",
      "all done trying to reduce usage of unit 114 final usage = 1.01971 697 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 958 with usage 1.01969 . Total units tried = 698\n",
      "all done trying to reduce usage of unit 958 final usage = 1.01969 698 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 310 with usage 1.01966 . Total units tried = 699\n",
      "all done trying to reduce usage of unit 310 final usage = 1.01966 698 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 1823 with usage 1.01965 . Total units tried = 700\n",
      "all done trying to reduce usage of unit 1823 final usage = 1.01965 698 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 199 with usage 1.01963 . Total units tried = 701\n",
      "all done trying to reduce usage of unit 199 final usage = 1.01963 698 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 1166 with usage 1.01961 . Total units tried = 702\n",
      "all done trying to reduce usage of unit 1166 final usage = 1.01961 698 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 1401 with usage 1.01961 . Total units tried = 703\n",
      "all done trying to reduce usage of unit 1401 final usage = 1.01961 699 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 710 with usage 1.0196 . Total units tried = 704\n",
      "all done trying to reduce usage of unit 710 final usage = 1.0196 699 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 500 with usage 1.0196 . Total units tried = 705\n",
      "all done trying to reduce usage of unit 500 final usage = 1.0196 699 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 440 with usage 1.01959 . Total units tried = 706\n",
      "all done trying to reduce usage of unit 440 final usage = 1.01959 699 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 620 with usage 1.01953 . Total units tried = 707\n",
      "all done trying to reduce usage of unit 620 final usage = 1.01953 699 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 673 with usage 1.01953 . Total units tried = 708\n",
      "all done trying to reduce usage of unit 673 final usage = 1.01953 700 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 1371 with usage 1.0195 . Total units tried = 709\n",
      "all done trying to reduce usage of unit 1371 final usage = 1.0195 700 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 973 with usage 1.0195 . Total units tried = 710\n",
      "all done trying to reduce usage of unit 973 final usage = 1.0195 700 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 1088 with usage 1.01949 . Total units tried = 711\n",
      "all done trying to reduce usage of unit 1088 final usage = 1.01949 700 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n",
      "starting to reduce usage for unit 1868 with usage 1.01947 . Total units tried = 712\n",
      "all done trying to reduce usage of unit 1868 final usage = 1.01947 700 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99751 0.08391\n"
     ]
    }
   ],
   "source": [
    "#SECOND PATCHING BLOCK -- OVERUSERS.  applies a suppression of LONG STRINGS.  \n",
    "print(\"we are still biased toward high overuse for large units, so do another round of overpatch\")\n",
    "#SECOND PATCHING BLOCK -- OVERUSERS.  applies a suppression of LONG STRINGS.  \n",
    "maxSD = 0.08 #0.06  #0.08  #0.04 \n",
    "print(maxSD,\"= default maxSD. Reco'ing 0.05 for blocky, 0.08 when only partially underpatched before this\")\n",
    "maxSD = float(input(\"enter maxSD\"))\n",
    "stopMaxUse = 1.02 #1.+  2.* maxSD  #0.5*maxSD  #don't be too aggressive; may create long chains\n",
    "print(\"default use threshold for moving on to next overused unit is\",r5(stopMaxUse) )\n",
    "stopMaxUse = float(input(\"enter updated stopMaxUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage above\",stopMaxUse)\n",
    "nBigUsers = 5\n",
    "maxExchangePop = 0.25*aDP  #0.15 or 0.25 is debatable.  use 0.15 unless blocky\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "print(\"this block reduces overuse, with exchanges up to\",int(maxExchangePop)) #\n",
    "maxGap = aDP - minDistrictPop  #0.9 * np.median(unitPop) # = aDP - minDistrictPop\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        maxNtries +=1\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "nonfrozenList = list()\n",
    "for t in popHDlist:\n",
    "    if t not in hasSplitUnit: #hasSplitUnit[t] != 1:  #don't mess with the small number of HDs that have to have a split muni\n",
    "        nonfrozenList.append(t)\n",
    "        \n",
    "attemptedBigUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to reduce up to\",maxNtries,\"units' overusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedBigUs) < maxNtries: \n",
    "    #simplified vs. vanillaHD - just take the biggest overuser\n",
    "    eligUnitUse = unitUse.copy()\n",
    "    for u in range(nUnits):\n",
    "        if u in attemptedBigUs: #we already tried this one, so lock it out\n",
    "            eligUnitUse[u] = -999.  #preventing re-selection\n",
    "    OUU = eligUnitUse.index(np.max(eligUnitUse))\n",
    "    attemptedBigUs.append(OUU)  #so we don't try this unit again in a future loop\n",
    "    if unitPop[OUU] > 0.25 * aDP:  #some overusers are high-pop.  If so, will drop only this unit, not any neighbors\n",
    "        OUUc = [OUU]\n",
    "    else:\n",
    "        OUUc = [OUU] + unitNbrs[OUU]\n",
    "    print(\"starting to reduce usage for unit\",OUU,\"with usage\",r5(unitUse[OUU]),\". Total units tried =\",len(attemptedBigUs) )\n",
    "    for u in OUUc.copy():\n",
    "        if unitUse[u] < 1.:\n",
    "            OUUc.remove(u)  #...drop any underused neighbors of the primary OUU from the target sheddable cluster\n",
    "    OUUcSet = set( OUUc)  #for muni-snap, overused are isolated.  So don't bother adding two-level neighbors to cluster\n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in nonfrozenList: #popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if OUU in HDunitList[t] and homeU[t] != OUU: #can't drop the home muni\n",
    "            HDadjoinSet =   set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            OUUcAdjoinSet = set( getAdjoiners(list(OUUcSet),unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(OUUcAdjoinSet) ) > 0: #the OUU or one of its overused 1-neighbors adjoins the complement\n",
    "                ouuHDs.append(t)  #so we can shed the OOUc to the complement\n",
    "                ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists)\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.8*len(ouuHDs) : #arbitrarily only examine 80% of HDs, biasing farthest ones       \n",
    "        if idxNo % 90 == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD out of\",len(ouuHDs),\"possible.  usage down to\",unitUse[OUU] )\n",
    "        idxNo -=1\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        thisLoopOUUc = [OUU]\n",
    "        possibleOUUc = list((  OUUcSet.intersection(set(HDunitList[t]) )  ).difference( {homeU[t]} )) #again, homeU is untouchable\n",
    "        if np.sum([unitPop[u] for u in possibleOUUc]) < 0.25*aDP:\n",
    "            thisLoopOUUc = possibleOUUc.copy() #we can add the whole non-HDincluded cluster (-homeU) without exceeding 0.25 aDP            \n",
    "        trySet = set(HDunitList[t]).difference(set(thisLoopOUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        if not (contig and complementContig):  #dropping the cluster creates a problem (likely an HD discontig).  Try something simpler ...\n",
    "            if len(set(unitNbrs[OUU]).intersection(HDadjoinSet) )  > 0:  #Yay! The OUU itself on border.  Try dropping just it, not the full cluster\n",
    "                HDouuSet = {OUU}\n",
    "                trySet = set(HDunitList[t]).difference( {OUU} )\n",
    "                contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        else:\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(thisLoopOUUc))  #set(OUUc)\n",
    "        if contig and complementContig:  #we can at least drop the cluster, let's go for more\n",
    "            #HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD.  Defined above\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = False            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop and u != homeU[t]:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            while HDouuCpop < maxExchangePop and not giveUpOnShedding:\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDouuCpop += unitPop[shedU]\n",
    "                        #print(\"shedding unit\",shedU,\"from HD\",t,\"total shed Pop now\", HDouuCpop)\n",
    "                        HDouuSet.add(shedU)\n",
    "                        trySet.remove(shedU)\n",
    "                        del shedScores[shedCandidates.index(shedU)]\n",
    "                        del shedCandidates[shedCandidates.index(shedU)]\n",
    "                        newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                        for u in newSheddables:\n",
    "                            if HDouuCpop + unitPop[u] <= maxExchangePop and u not in shedCandidates and u != homeU[t]:\n",
    "                                shedCandidates.append(u)\n",
    "                                shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True \n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                nearHDscore2 = nearHDscore.copy()\n",
    "                for ij, uu in enumerate(nearHDlist):\n",
    "                    nHDnbrs = len( set(unitNbrs[uu]).intersection(trySet) )\n",
    "                    if nHDnbrs == 1 :  #BIAS AGAINST CREATING A SLIM CHAIN\n",
    "                        nearHDscore2[ij] *= 0.5   #make this score closer to zero (less negative) \n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore2), 0, True   #originally, used nearHDscore itself here\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...                            \n",
    "                    canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            if abs(currPop - aDP) <= maxGap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "35140f52-2cb9-40e6-be15-ac02ba88316c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " patched use avg 0.99751 and SD 0.08391\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 8941\n",
      "working on HD 800 out of 8941\n",
      "working on HD 1600 out of 8941\n",
      "working on HD 2400 out of 8941\n",
      "working on HD 3200 out of 8941\n",
      "working on HD 4000 out of 8941\n",
      "working on HD 4800 out of 8941\n",
      "working on HD 5600 out of 8941\n",
      "working on HD 6400 out of 8941\n",
      "working on HD 7200 out of 8941\n",
      "working on HD 8000 out of 8941\n",
      "working on HD 8800 out of 8941\n",
      "all done checking HD and complement contiguity for all 8941 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse, HDvPop = [0.]*nUnits, [0.]*nUnits, [0.]*nHDs\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts       \n",
    "        HDvPop[t] += unitPop[u]\n",
    "    if hasSplitUnit[t] == 1:\n",
    "        unitUse[splitUnitNo[t]] -= (1. - splitUnitFrac[t]) * nDistricts * HDweight[t]\n",
    "        HDvPop[t]               -= (1. - splitUnitFrac[t]) * unitPop[splitUnitNo[t]]\n",
    "#    for u in unpatchedHDlist[t]:\n",
    "#        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "#plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "#unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\" patched use avg\", r5(patchedAvg),\"and SD\",r5(patchedSD) )\n",
    "#print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.axvline(0.95*aDP, ls=\"dotted\")\n",
    "plt.axvline(1.05*aDP, ls=\"dotted\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%800 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "92e9e7c1-c3a3-4039-a1d7-1dfe5d9a727b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "patched save in HDunitList format\n"
     ]
    }
   ],
   "source": [
    "print(\"patched save in HDunitList format\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "outDF = pd.DataFrame( {\"vtd\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"splitUnitNo\":splitUnitNo,\"splitUnitFrac\":splitUnitFrac,\n",
    "                       \"centroid x\":HDCPx, \"centroid y\":HDCPy, \"homeU\":homeU, \"homeC\":countyNo } )\n",
    "outname = STATE+str(int(nHDs))+\"_\"+str(nDistricts)+\"schoolPatch084.csv\" \n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "2f2b0f2e-20ed-4220-846e-4a0bf687d1d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now write the final vtd lists to a file.  In this version, we had no splits, so assign vtds here\n"
     ]
    }
   ],
   "source": [
    "print(\"now write the final vtd lists to a file.  In this version, we had no splits, so assign vtds here\")\n",
    "finalVTDlist = [list() for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        finalVTDlist[t] += unitTractList[u]\n",
    "tList = [t for t in range(nHDs)]\n",
    "outDF = pd.DataFrame( {\"vtd\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":finalVTDlist,\n",
    "                      \"centroid x\":HDCPx, \"centroid y\":HDCPy,\"countyNo\":countyNo,\"splitUnitNo\":splitUnitNo} )\n",
    "outname = STATE+str(int(nHDs))+\"_\"+str(nDistricts)+\"schoolVTDs_084.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)  #muni-based complete 11-11-24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "164189ee-30cf-4cb2-80a9-22195eff585c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from here, could patch more, likely requires split units.   Would need to copy from another code"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
